Immersive 3D Visualization is a Java application that allows users to view remote sensing data like aerial images in 3D. It uses Java 3D Technology and OpenGL to render 2D images into 3D by combining the image data with digital elevation models (DEM) that provide height information. This allows any region of interest to be viewed in 3D rather than just 2D images. The application can display 3D models of terrain, buildings, and vegetation to create an immersive 3D visualization of remote areas. It provides tools for simulation and fly-through of 3D environments built from remote sensing data.
This document defines and describes Digital Elevation Models (DEMs). It discusses that DEMs are 3D representations of land surface elevation from various data sources. There are two main types of DEMs - raster and vector (TIN). Data can be captured through remote sensing, photogrammetry, or land surveys. Free global DEMs are available from sources like SRTM, ASTER, and ALOS. DEMs have many applications including terrain analysis, hydrology, mapping, and more.
High Performance Computing for Satellite Image Processing and Analyzing – A ...Editor IJCATR
High Performance Computing (HPC) is the recently developed technology in the field of computer science, which evolved
due to meet increasing demands for processing speed and analysing/processing huge size of data sets. HPC brings together several
technologies such as computer architecture, algorithm, programs and system software under one canopy to solve/handle advanced
complex problems quickly and effectively. It is a crucial element today to gather and process large amount of satellite (remote sensing)
data which is the need of an hour. In this paper, we review recent development in HPC technology (Parallel, Distributed and Cluster
Computing) for satellite data processing and analysing. We attempt to discuss the fundamentals of High Performance Computing
(HPC) for satellite data processing and analysing, in a way which is easy to understand without much previous background. We sketch
the various HPC approach such as Parallel, Distributed & Cluster Computing and subsequent satellite data processing & analysing
methods like geo-referencing, image mosaicking, image classification, image fusion and Morphological/neural approach for hyperspectral satellite data. Collective, these works deliver a snapshot, tables and algorithms of the recent developments in those sectors and
offer a thoughtful perspective of the potential and promising challenges of satellite data processing and analysing using HPC
paradigms.
This document provides an overview of remote sensing and image interpretation. It discusses key topics such as the use of maps as models to represent features on Earth, different types of map scales and spatial referencing systems, and how computers are used in map production. It also outlines the process of image interpretation, including levels of interpretation keys and basic elements to examine like size, shape, shadow, tone, color, and texture. Software programs used in map production like ArcGIS and types of data products from remote sensing are also reviewed.
This document summarizes applications of remote sensing for digital elevation models. It discusses how remote sensing uses electromagnetic rays to acquire data without physical contact. Digital elevation models are created using remote sensing techniques to represent terrain and are built systematically or randomly. Methods for creating DEMs include interpolation of contours or using radar data from two passes of a satellite or a single pass with two antennas. The quality depends on factors like terrain roughness and pixel size. Common software used includes TacitView, Socet GXP, and IDRISI.
This document discusses digital image processing of satellite images. It describes how satellite images are represented digitally as pixels with brightness values. It outlines three main categories of image processing: image rectification and restoration to correct distortions; enhancement to improve visual interpretation; and information extraction to automate feature identification. Specific techniques discussed include image rectification, contrast enhancement, spatial filtering, edge enhancement, and band ratioing. The overall aim is to analyze satellite images both visually and quantitatively.
Stereo Correspondence Algorithms for Robotic Applications Under Ideal And Non...CSCJournals
The use of visual information in real time applications such as in robotic pick, navigation, obstacle avoidance etc. has been widely used in many sectors for enabling them to interact with its environment. Robotics require computationally simpler and easy to implement stereo vision algorithms that will provide reliable and accurate results under real time constraint. Stereo vision is a less expensive, passive sensing technique, for inferring the three dimensional position of objects from two or more simultaneous views of a scene and there is no interference with other sensing devices if multiple robots are present in the same environment. Stereo correspondence aims at finding matching points in the stereo image pair based on Lambertian criteria to obtain disparity. The correspondence algorithm will provide high resolution disparity maps of the scene by comparing two views of the scene under the study. By using the principle of triangulation and with the help of camera parameters, depth information can be extracted from this disparity .Since the focus is on real-time application, only the local stereo correspondence algorithms are considered. A comparative study based on error and computational costs are done between two area based algorithms. Evaluation of Sum of absolute Difference algorithm, which is less computationally expensive, suitable for ideal lightening condition and a more accurate adaptive binary support window algorithm that can handle of non-ideal lighting conditions are taken for this study. To simplify the correspondence search, rectified stereo image pairs are used as inputs.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
International Journal of Modern Engineering Research (IJMER) covers all the fields of engineering and science: Electrical Engineering, Mechanical Engineering, Civil Engineering, Chemical Engineering, Computer Engineering, Agricultural Engineering, Aerospace Engineering, Thermodynamics, Structural Engineering, Control Engineering, Robotics, Mechatronics, Fluid Mechanics, Nanotechnology, Simulators, Web-based Learning, Remote Laboratories, Engineering Design Methods, Education Research, Students' Satisfaction and Motivation, Global Projects, and Assessment…. And many more.
This document defines and describes Digital Elevation Models (DEMs). It discusses that DEMs are 3D representations of land surface elevation from various data sources. There are two main types of DEMs - raster and vector (TIN). Data can be captured through remote sensing, photogrammetry, or land surveys. Free global DEMs are available from sources like SRTM, ASTER, and ALOS. DEMs have many applications including terrain analysis, hydrology, mapping, and more.
High Performance Computing for Satellite Image Processing and Analyzing – A ...Editor IJCATR
High Performance Computing (HPC) is the recently developed technology in the field of computer science, which evolved
due to meet increasing demands for processing speed and analysing/processing huge size of data sets. HPC brings together several
technologies such as computer architecture, algorithm, programs and system software under one canopy to solve/handle advanced
complex problems quickly and effectively. It is a crucial element today to gather and process large amount of satellite (remote sensing)
data which is the need of an hour. In this paper, we review recent development in HPC technology (Parallel, Distributed and Cluster
Computing) for satellite data processing and analysing. We attempt to discuss the fundamentals of High Performance Computing
(HPC) for satellite data processing and analysing, in a way which is easy to understand without much previous background. We sketch
the various HPC approach such as Parallel, Distributed & Cluster Computing and subsequent satellite data processing & analysing
methods like geo-referencing, image mosaicking, image classification, image fusion and Morphological/neural approach for hyperspectral satellite data. Collective, these works deliver a snapshot, tables and algorithms of the recent developments in those sectors and
offer a thoughtful perspective of the potential and promising challenges of satellite data processing and analysing using HPC
paradigms.
This document provides an overview of remote sensing and image interpretation. It discusses key topics such as the use of maps as models to represent features on Earth, different types of map scales and spatial referencing systems, and how computers are used in map production. It also outlines the process of image interpretation, including levels of interpretation keys and basic elements to examine like size, shape, shadow, tone, color, and texture. Software programs used in map production like ArcGIS and types of data products from remote sensing are also reviewed.
This document summarizes applications of remote sensing for digital elevation models. It discusses how remote sensing uses electromagnetic rays to acquire data without physical contact. Digital elevation models are created using remote sensing techniques to represent terrain and are built systematically or randomly. Methods for creating DEMs include interpolation of contours or using radar data from two passes of a satellite or a single pass with two antennas. The quality depends on factors like terrain roughness and pixel size. Common software used includes TacitView, Socet GXP, and IDRISI.
This document discusses digital image processing of satellite images. It describes how satellite images are represented digitally as pixels with brightness values. It outlines three main categories of image processing: image rectification and restoration to correct distortions; enhancement to improve visual interpretation; and information extraction to automate feature identification. Specific techniques discussed include image rectification, contrast enhancement, spatial filtering, edge enhancement, and band ratioing. The overall aim is to analyze satellite images both visually and quantitatively.
Stereo Correspondence Algorithms for Robotic Applications Under Ideal And Non...CSCJournals
The use of visual information in real time applications such as in robotic pick, navigation, obstacle avoidance etc. has been widely used in many sectors for enabling them to interact with its environment. Robotics require computationally simpler and easy to implement stereo vision algorithms that will provide reliable and accurate results under real time constraint. Stereo vision is a less expensive, passive sensing technique, for inferring the three dimensional position of objects from two or more simultaneous views of a scene and there is no interference with other sensing devices if multiple robots are present in the same environment. Stereo correspondence aims at finding matching points in the stereo image pair based on Lambertian criteria to obtain disparity. The correspondence algorithm will provide high resolution disparity maps of the scene by comparing two views of the scene under the study. By using the principle of triangulation and with the help of camera parameters, depth information can be extracted from this disparity .Since the focus is on real-time application, only the local stereo correspondence algorithms are considered. A comparative study based on error and computational costs are done between two area based algorithms. Evaluation of Sum of absolute Difference algorithm, which is less computationally expensive, suitable for ideal lightening condition and a more accurate adaptive binary support window algorithm that can handle of non-ideal lighting conditions are taken for this study. To simplify the correspondence search, rectified stereo image pairs are used as inputs.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
International Journal of Modern Engineering Research (IJMER) covers all the fields of engineering and science: Electrical Engineering, Mechanical Engineering, Civil Engineering, Chemical Engineering, Computer Engineering, Agricultural Engineering, Aerospace Engineering, Thermodynamics, Structural Engineering, Control Engineering, Robotics, Mechatronics, Fluid Mechanics, Nanotechnology, Simulators, Web-based Learning, Remote Laboratories, Engineering Design Methods, Education Research, Students' Satisfaction and Motivation, Global Projects, and Assessment…. And many more.
Estimation of 3d Visualization for Medical Machinary Imagestheijes
This document discusses a study on 3D visualization of medical images. It presents four key modules: 1) 3D viewer using geometry and color information to generate 3D images from 2D X-ray images, 2) volume viewer representing anatomical structures as 3D distributions, 3) interactive 3D surface plot using image luminance as height, and 4) stack surface plot displaying 3D graphs of pixel intensities in image stacks. The study aims to improve analysis of medical imaging data through 3D visualization techniques.
Satellite Image Processing technique to enhance raw images received from cameras or sensors placed on satellites, space probes and aircrafts or pictures taken in normal day to day life in various applications.
This document discusses different methods for representing digital terrain, including grids, TINs, quadtrees, and multi-resolution models. Grid DEMs represent terrain as a regular grid of elevation postings. TINs use an irregular network of triangles to connect elevation postings. Quadtrees adapt resolution based on terrain complexity. Multi-resolution models provide multiple levels of detail for large terrain datasets. Each method has advantages like storage efficiency or terrain adaptation and disadvantages like processing costs or irregularity. The best method depends on the application and dataset characteristics.
Introduction to image processing-Class NotesDr.YNM
This document provides an introduction and overview of digital image processing. It discusses key concepts such as how digital images are composed of pixels and how image processing is used to enhance images by removing noise and irregularities. It then describes several fields that use digital image processing techniques, including medical imaging using X-rays, gamma rays, and infrared light, as well as applications in remote sensing, fingerprint analysis, and radar imaging.
Image processing involves processing images in a desired manner by obtaining an image in a readable format from sources like the Internet. The digital image can then be optimized for the intended application by enhancing or altering structures within it based on factors like the body part, diagnostic task, or viewing preferences. Some examples of image processing include enhancing images to make them more useful or pleasing, restoring images by removing things like blurriness or grid lines, and decompressing compressed image data or reconstructing image slices from scans.
This document discusses digital elevation models (DEMs), including how they are generated from remote sensing data like satellite imagery and LiDAR, their typical accuracies, and common uses. DEMs can be created from aerial or satellite stereo images, radar interferometry, or terrestrial land surveying. They are used to produce topographic maps and orthophotos, model flooding, perform visibility analysis, and create 3D terrain representations. The quality and resolution of DEMs varies depending on the source data and techniques used.
COMPLETE END-TO-END LOW COST SOLUTION TO A 3D SCANNING SYSTEM WITH INTEGRATED...ijcsit
3D reconstruction is a technique used in computer vision which has a wide range of applications in
areas like object recognition, city modelling, virtual reality, physical simulations, video games and
special effects. Previously, to perform a 3D reconstruction, specialized hardwares were required.
Such systems were often very expensive and was only available for industrial or research purpose.
With the rise of the availability of high-quality low cost 3D sensors, it is now possible to design
inexpensive complete 3D scanning systems. The objective of this work was to design an acquisition and
processing system that can perform 3D scanning and reconstruction of objects seamlessly. In addition,
the goal of this work also included making the 3D scanning process fully automated by building and
integrating a turntable alongside the software. This means the user can perform a full 3D scan only by
a press of a few buttons from our dedicated graphical user interface. Three main steps were followed
to go from acquisition of point clouds to the finished reconstructed 3D model. First, our system
acquires point cloud data of a person/object using inexpensive camera sensor. Second, align and
convert the acquired point cloud data into a watertight mesh of good quality. Third, export the
reconstructed model to a 3D printer to obtain a proper 3D print of the model.
A Digital Terrain Model (DTM) is a digital file that provides a detailed 3D representation of the topography of the Earth's surface. It consists of terrain elevations at regularly spaced intervals that can be used to create 3D visualizations and analyze slope, aspect, height, and other topographical features. DTMs with draped aerial imagery can help with planning, engineering, and environmental impact assessments by providing accurate 3D models of land surfaces. They are used across a variety of industries and applications.
This document discusses techniques for 3D image visualization. It begins with an introduction and covers topics like rendering techniques, MATLAB visualization, volume rendering, isocontouring, hole detection, and applications of stereoscopic visualization. The document outlines various methods for 3D output like projection and OpenGL libraries. It discusses advantages like hardware support for 3D graphics and disadvantages such as objects being drawn as 2D. The conclusion states that while techniques exist, more research is still needed for innovative 3D visualization of diverse data types.
A hybrid gwr based height estimation method for buildingAbery Au
This document describes a hybrid GWR-based method for building detection using sparse LiDAR data and aerial photos. It begins by classifying the LiDAR data and performing unsupervised image classification of the aerial photo, which cannot fully separate buildings from other surfaces. It then uses GWR to estimate height values for all pixels by integrating characteristics from LiDAR and aerial photo data. GWR models relate height values from LiDAR to spectral values of surrounding pixels based on distance. This predicts height rasters to extract building pixels clustered above a certain height threshold. The method was tested on Florida data and found more accurate than single-data classification.
Accuracy checks in the production of orthophotosAlexander Decker
This document summarizes the process of creating an orthophoto and factors that affect its geometric accuracy. It describes preparing input data like aerial photos, camera calibration, and ground control points. It then details the steps of interior and exterior orientation, automatic DTM generation with editing, and orthophoto generation. Accuracy is expected to be within 1 meter horizontally and 0.4 meters vertically based on the input data and DTM error. Planimetric accuracy of digitized features from the orthophoto will also depend on these geometric accuracy factors.
International Refereed Journal of Engineering and Science (IRJES)irjes
This document discusses using MATLAB to process weather satellite images. It begins by introducing weather satellites and the different types of images they provide. It then demonstrates how to create black/white, grayscale, and color images in MATLAB. The document shows how to read weather images into MATLAB and check if they are grayscale or RGB color. Methods for enhancing images through noise filtering and adjusting contrast are presented. In summary, the document explores using MATLAB for processing various types of weather satellite images.
Lets Understand the overall working of photogrammetry. Get ideas about photogrammetry branches. Also, check the difference between photogrammetry and remote sensing.
Computer vision analyzes real-world images while machine vision uses simplified images. Edge detection locates object edges by analyzing pixel values. Shape detection identifies shapes by counting continuous edges and measuring angles between lines. Motion detection compares pixel positions between frames to detect motion if the pixel mass changes significantly. Optical flow analyzes pixel intensity changes between images to determine motion vectors without identifying objects. Aerial robot altitude can be estimated from a downward camera by analyzing pixel velocity, as higher altitude results in slower apparent ground motion.
This document is a thesis submitted by Gagandeep Singh for his M.Tech degree in RS & GIS from NIT Warangal in 2013-2015. It discusses various topics related to digital terrain modeling including contour lines, grid DTMs, TINs, the differences between DSMs and DEMs, data acquisition methods, processing techniques, and applications of digital terrain data. It also evaluates different data sources for terrain modeling like SRTM, topographic maps, and Google Earth imagery and assesses their accuracy through statistical analysis and visual inspection.
This document summarizes research on using 3D geometry to organize and analyze large collections of 2D imagery. It begins by discussing how digital camera technology has advanced, enabling the collection of billions of photos and videos but current capabilities for analyzing imagery lag behind. 3D geometry provides a framework to relate images taken at different times and perspectives. The document then demonstrates several applications of 3D imagery exploitation, beginning with constructing a panoramic mosaic from photos taken by a stationary camera for perimeter surveillance. It shows how video frames can be aligned to the mosaic in real time.
A comparative study of histogram equalization based image enhancement techniq...sipij
Histogram Equalization is a contrast enhancement te
chnique in the image processing which uses the
histogram of image. However histogram equalization
is not the best method for contrast enhancement
because the mean brightness of the output image is
significantly different from the input image. There
are
several extensions of histogram equalization has be
en proposed to overcome the brightness preservation
challenge. Contrast enhancement using brightness pr
eserving bi-histogram equalization (BBHE) and
Dualistic sub image histogram equalization (DSIHE)
which divides the image histogram into two parts
based on the input mean and median respectively the
n equalizes each sub histogram independently. This
paper provides review of different popular histogra
m equalization techniques and experimental study ba
sed
on the absolute mean brightness error (AMBE), peak
signal to noise ratio (PSNR), Structure similarity
index
(SSI) and Entropy.
Happy Camera Club report for Ashwini Charitable Trust Workshop April - May 2013HappyCameraClub
HCC trained a varied group of 12 between the age group 14-19 on the basics of Photography, use of Light, composition through editing and various elements, framing, how to download pictures, Field exercises - street photography basics, portraits,
analysis of works of different photographers and basics of light room or Photoshop
HappyCameraClub is a social organization committed to providing an opportunity to the underprivileged by teaching them photography which not only will improve their self confidence and interpersonal skills but also use this as a knowledge for future lifelyhood purposes.
Face detection using the 3 x3 block rank patterns of gradient magnitude imagessipij
Face detection locates faces prior to various face-
related applications. The objective of face detecti
on is to
determine whether or not there are any faces in an
image and, if any, the location of each face is det
ected.
Face detection in real images is challenging due to
large variability of illumination and face appeara
nces.
This paper proposes a face detection algorithm usin
g the 3×3 block rank patterns of gradient magnitude
images and a geometrical face model. First, the ill
umination-corrected image of the face region is obt
ained
using the brightness plane that is produced using t
he locally minimum brightness of each block. Next,
the
illumination-corrected image is histogram equalized
, the face region is divided into nine (3×3) blocks
, and
two directional (horizontal and vertical) gradient
magnitude images are computed, from which the 3×3
block rank patterns are obtained. For face detectio
n, using the FERET and GT databases three types of
the
3×3 block rank patterns are a priori determined as
templates based on the distribution of the sum of t
he
gradient magnitudes of each block in the face candi
date region that is also composed of nine (3×3) blo
cks.
The 3×3 block rank patterns roughly classify whethe
r the detected face candidate region contains a fac
e or
not. Finally, facial features are detected and used
to validate the face model. The face candidate is
validated as a face if it is matched with the geome
trical face model. The proposed algorithm is tested
on the
Caltech database images and real images. Experiment
al results with a number of test images show the
effectiveness of the proposed algorithm.
Vehicle detection and tracking techniques a concise reviewsipij
Vehicle detection and tracking applications play an important role for civilian and military applications
such as in highway traffic surveillance control, management and urban traffic planning. Vehicle detection
process on road are used for vehicle tracking, counts, average speed of each individual vehicle, traffic
analysis and vehicle categorizing objectives and may be implemented under different environments
changes. In this review, we present a concise overview of image processing methods and analysis tools
which used in building these previous mentioned applications that involved developing traffic surveillance
systems. More precisely and in contrast with other reviews, we classified the processing methods under
three categories for more clarification to explain the traffic systems.
Estimation of 3d Visualization for Medical Machinary Imagestheijes
This document discusses a study on 3D visualization of medical images. It presents four key modules: 1) 3D viewer using geometry and color information to generate 3D images from 2D X-ray images, 2) volume viewer representing anatomical structures as 3D distributions, 3) interactive 3D surface plot using image luminance as height, and 4) stack surface plot displaying 3D graphs of pixel intensities in image stacks. The study aims to improve analysis of medical imaging data through 3D visualization techniques.
Satellite Image Processing technique to enhance raw images received from cameras or sensors placed on satellites, space probes and aircrafts or pictures taken in normal day to day life in various applications.
This document discusses different methods for representing digital terrain, including grids, TINs, quadtrees, and multi-resolution models. Grid DEMs represent terrain as a regular grid of elevation postings. TINs use an irregular network of triangles to connect elevation postings. Quadtrees adapt resolution based on terrain complexity. Multi-resolution models provide multiple levels of detail for large terrain datasets. Each method has advantages like storage efficiency or terrain adaptation and disadvantages like processing costs or irregularity. The best method depends on the application and dataset characteristics.
Introduction to image processing-Class NotesDr.YNM
This document provides an introduction and overview of digital image processing. It discusses key concepts such as how digital images are composed of pixels and how image processing is used to enhance images by removing noise and irregularities. It then describes several fields that use digital image processing techniques, including medical imaging using X-rays, gamma rays, and infrared light, as well as applications in remote sensing, fingerprint analysis, and radar imaging.
Image processing involves processing images in a desired manner by obtaining an image in a readable format from sources like the Internet. The digital image can then be optimized for the intended application by enhancing or altering structures within it based on factors like the body part, diagnostic task, or viewing preferences. Some examples of image processing include enhancing images to make them more useful or pleasing, restoring images by removing things like blurriness or grid lines, and decompressing compressed image data or reconstructing image slices from scans.
This document discusses digital elevation models (DEMs), including how they are generated from remote sensing data like satellite imagery and LiDAR, their typical accuracies, and common uses. DEMs can be created from aerial or satellite stereo images, radar interferometry, or terrestrial land surveying. They are used to produce topographic maps and orthophotos, model flooding, perform visibility analysis, and create 3D terrain representations. The quality and resolution of DEMs varies depending on the source data and techniques used.
COMPLETE END-TO-END LOW COST SOLUTION TO A 3D SCANNING SYSTEM WITH INTEGRATED...ijcsit
3D reconstruction is a technique used in computer vision which has a wide range of applications in
areas like object recognition, city modelling, virtual reality, physical simulations, video games and
special effects. Previously, to perform a 3D reconstruction, specialized hardwares were required.
Such systems were often very expensive and was only available for industrial or research purpose.
With the rise of the availability of high-quality low cost 3D sensors, it is now possible to design
inexpensive complete 3D scanning systems. The objective of this work was to design an acquisition and
processing system that can perform 3D scanning and reconstruction of objects seamlessly. In addition,
the goal of this work also included making the 3D scanning process fully automated by building and
integrating a turntable alongside the software. This means the user can perform a full 3D scan only by
a press of a few buttons from our dedicated graphical user interface. Three main steps were followed
to go from acquisition of point clouds to the finished reconstructed 3D model. First, our system
acquires point cloud data of a person/object using inexpensive camera sensor. Second, align and
convert the acquired point cloud data into a watertight mesh of good quality. Third, export the
reconstructed model to a 3D printer to obtain a proper 3D print of the model.
A Digital Terrain Model (DTM) is a digital file that provides a detailed 3D representation of the topography of the Earth's surface. It consists of terrain elevations at regularly spaced intervals that can be used to create 3D visualizations and analyze slope, aspect, height, and other topographical features. DTMs with draped aerial imagery can help with planning, engineering, and environmental impact assessments by providing accurate 3D models of land surfaces. They are used across a variety of industries and applications.
This document discusses techniques for 3D image visualization. It begins with an introduction and covers topics like rendering techniques, MATLAB visualization, volume rendering, isocontouring, hole detection, and applications of stereoscopic visualization. The document outlines various methods for 3D output like projection and OpenGL libraries. It discusses advantages like hardware support for 3D graphics and disadvantages such as objects being drawn as 2D. The conclusion states that while techniques exist, more research is still needed for innovative 3D visualization of diverse data types.
A hybrid gwr based height estimation method for buildingAbery Au
This document describes a hybrid GWR-based method for building detection using sparse LiDAR data and aerial photos. It begins by classifying the LiDAR data and performing unsupervised image classification of the aerial photo, which cannot fully separate buildings from other surfaces. It then uses GWR to estimate height values for all pixels by integrating characteristics from LiDAR and aerial photo data. GWR models relate height values from LiDAR to spectral values of surrounding pixels based on distance. This predicts height rasters to extract building pixels clustered above a certain height threshold. The method was tested on Florida data and found more accurate than single-data classification.
Accuracy checks in the production of orthophotosAlexander Decker
This document summarizes the process of creating an orthophoto and factors that affect its geometric accuracy. It describes preparing input data like aerial photos, camera calibration, and ground control points. It then details the steps of interior and exterior orientation, automatic DTM generation with editing, and orthophoto generation. Accuracy is expected to be within 1 meter horizontally and 0.4 meters vertically based on the input data and DTM error. Planimetric accuracy of digitized features from the orthophoto will also depend on these geometric accuracy factors.
International Refereed Journal of Engineering and Science (IRJES)irjes
This document discusses using MATLAB to process weather satellite images. It begins by introducing weather satellites and the different types of images they provide. It then demonstrates how to create black/white, grayscale, and color images in MATLAB. The document shows how to read weather images into MATLAB and check if they are grayscale or RGB color. Methods for enhancing images through noise filtering and adjusting contrast are presented. In summary, the document explores using MATLAB for processing various types of weather satellite images.
Lets Understand the overall working of photogrammetry. Get ideas about photogrammetry branches. Also, check the difference between photogrammetry and remote sensing.
Computer vision analyzes real-world images while machine vision uses simplified images. Edge detection locates object edges by analyzing pixel values. Shape detection identifies shapes by counting continuous edges and measuring angles between lines. Motion detection compares pixel positions between frames to detect motion if the pixel mass changes significantly. Optical flow analyzes pixel intensity changes between images to determine motion vectors without identifying objects. Aerial robot altitude can be estimated from a downward camera by analyzing pixel velocity, as higher altitude results in slower apparent ground motion.
This document is a thesis submitted by Gagandeep Singh for his M.Tech degree in RS & GIS from NIT Warangal in 2013-2015. It discusses various topics related to digital terrain modeling including contour lines, grid DTMs, TINs, the differences between DSMs and DEMs, data acquisition methods, processing techniques, and applications of digital terrain data. It also evaluates different data sources for terrain modeling like SRTM, topographic maps, and Google Earth imagery and assesses their accuracy through statistical analysis and visual inspection.
This document summarizes research on using 3D geometry to organize and analyze large collections of 2D imagery. It begins by discussing how digital camera technology has advanced, enabling the collection of billions of photos and videos but current capabilities for analyzing imagery lag behind. 3D geometry provides a framework to relate images taken at different times and perspectives. The document then demonstrates several applications of 3D imagery exploitation, beginning with constructing a panoramic mosaic from photos taken by a stationary camera for perimeter surveillance. It shows how video frames can be aligned to the mosaic in real time.
A comparative study of histogram equalization based image enhancement techniq...sipij
Histogram Equalization is a contrast enhancement te
chnique in the image processing which uses the
histogram of image. However histogram equalization
is not the best method for contrast enhancement
because the mean brightness of the output image is
significantly different from the input image. There
are
several extensions of histogram equalization has be
en proposed to overcome the brightness preservation
challenge. Contrast enhancement using brightness pr
eserving bi-histogram equalization (BBHE) and
Dualistic sub image histogram equalization (DSIHE)
which divides the image histogram into two parts
based on the input mean and median respectively the
n equalizes each sub histogram independently. This
paper provides review of different popular histogra
m equalization techniques and experimental study ba
sed
on the absolute mean brightness error (AMBE), peak
signal to noise ratio (PSNR), Structure similarity
index
(SSI) and Entropy.
Happy Camera Club report for Ashwini Charitable Trust Workshop April - May 2013HappyCameraClub
HCC trained a varied group of 12 between the age group 14-19 on the basics of Photography, use of Light, composition through editing and various elements, framing, how to download pictures, Field exercises - street photography basics, portraits,
analysis of works of different photographers and basics of light room or Photoshop
HappyCameraClub is a social organization committed to providing an opportunity to the underprivileged by teaching them photography which not only will improve their self confidence and interpersonal skills but also use this as a knowledge for future lifelyhood purposes.
Face detection using the 3 x3 block rank patterns of gradient magnitude imagessipij
Face detection locates faces prior to various face-
related applications. The objective of face detecti
on is to
determine whether or not there are any faces in an
image and, if any, the location of each face is det
ected.
Face detection in real images is challenging due to
large variability of illumination and face appeara
nces.
This paper proposes a face detection algorithm usin
g the 3×3 block rank patterns of gradient magnitude
images and a geometrical face model. First, the ill
umination-corrected image of the face region is obt
ained
using the brightness plane that is produced using t
he locally minimum brightness of each block. Next,
the
illumination-corrected image is histogram equalized
, the face region is divided into nine (3×3) blocks
, and
two directional (horizontal and vertical) gradient
magnitude images are computed, from which the 3×3
block rank patterns are obtained. For face detectio
n, using the FERET and GT databases three types of
the
3×3 block rank patterns are a priori determined as
templates based on the distribution of the sum of t
he
gradient magnitudes of each block in the face candi
date region that is also composed of nine (3×3) blo
cks.
The 3×3 block rank patterns roughly classify whethe
r the detected face candidate region contains a fac
e or
not. Finally, facial features are detected and used
to validate the face model. The face candidate is
validated as a face if it is matched with the geome
trical face model. The proposed algorithm is tested
on the
Caltech database images and real images. Experiment
al results with a number of test images show the
effectiveness of the proposed algorithm.
Vehicle detection and tracking techniques a concise reviewsipij
Vehicle detection and tracking applications play an important role for civilian and military applications
such as in highway traffic surveillance control, management and urban traffic planning. Vehicle detection
process on road are used for vehicle tracking, counts, average speed of each individual vehicle, traffic
analysis and vehicle categorizing objectives and may be implemented under different environments
changes. In this review, we present a concise overview of image processing methods and analysis tools
which used in building these previous mentioned applications that involved developing traffic surveillance
systems. More precisely and in contrast with other reviews, we classified the processing methods under
three categories for more clarification to explain the traffic systems.
A new hybrid method for the segmentation of the brain mrissipij
The magnetic resonance imaging is a method which has undeniable qualities of contrast and tissue
characterization, presenting an interest in the follow-up of various pathologies such as the multiple
sclerosis. In this work, a new method of hybrid segmentation is presented and applied to Brain MRIs. The
extraction of the image of the brain is pretreated with the Non Local Means filter. A theoretical approach is
proposed; finally the last section is organized around an experimental part allowing the study of the
behavior of our model on textured images. In the aim to validate our model, different segmentations were
down on pathological Brain MRI, the obtained results have been compared to the results obtained by
another models. This results show the effectiveness and the robustness of the suggested approach.
Design and implementation of video tracking system based on camera field of viewsipij
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View (CFOV), Otsu’s method was used to detect targets such as vehicles and people. Whereas most
algorithms were spent a lot of time to execute the process, an algorithm was developed to achieve it in a
little time. The histogram projection was used in both directional to detect target from search region,
which is robust to various light conditions in Charge Couple Device (CCD) camera images and saves
computation time.
Our algorithm based on background subtraction, and normalize cross correlation operation from a series
of sequential sub images can estimate the motion vector. Camera field of view (CFOV) was determined and
calibrated to find the relation between real distance and image distance. The system was tested by
measuring the real position of object in the laboratory and compares it with the result of computed one. So
these results are promising to develop the system in future.
Fast nas rif algorithm using iterative conjugate gradient methodsipij
This summarizes a document describing the FAST NAS-RIF algorithm using an iterative conjugate gradient method for image restoration.
1) The NAS-RIF algorithm iteratively estimates image pixels and the point spread function based on the conjugate gradient method, without assuming parametric models.
2) The paper proposes updating the conjugate gradient method's parameters and objective function to improve minimization of the cost function and reduce execution time.
3) Experimental results comparing updated and original conjugate gradient parameters show improved restoration effect and higher peak signal-to-noise ratio with the updates.
Happy Camera Club is a social enterprise that provides excellence of tutoring and imparting life skills to underprivileged individuals .
It also connects with many people in everyday life through photography as a medium .
we can be reached at special@happycameraclub.com
Intelligent indoor mobile robot navigation using stereo visionsipij
Majority of the existing robot navigation systems, which facilitate the use of laser range finders, sonar
sensors or artificial landmarks, has the ability to locate itself in an unknown environment and then build a
map of the corresponding environment. Stereo vision,while still being a rapidly developing technique in the
field of autonomous mobile robots, are currently less preferable due to its high implementation cost. This
paper aims at describing an experimental approach for the building of a stereo vision system that helps the
robots to avoid obstacles and navigate through indoor environments and at the same time remaining very
much cost effective. This paper discusses the fusion techniques of stereo vision and ultrasound sensors
which helps in the successful navigation through different types of complex environments. The data from
the sensor enables the robot to create the two dimensional topological map of unknown environments and
stereo vision systems models the three dimension model of the same environment.
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Franky and Fobert were fishing by a lake when Fobert suggested playing football instead. When Franky threw the football, it frightened a flock of flamingos, causing them to fly into a frenzy. The flamingos' chaotic behavior resulted in feathers flying through the air and some of them falling over or freezing in fear. To make amends, Franky and Fobert fed the flamingos fried fish by the fire, and they all became friends.
Extraction of spots in dna microarrays using genetic algorithmsipij
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crucial issue since biological interpretations depend on it. The image analysis starts with the formation of
grid, which is a laborious process requiring human intervention. This paper presents a method for optimal
search of the spots using genetic algorithm without formation of grid. The information of every spot is
extracted by obtaining a pixel belonging to that spot. The method developed selects pixels of high intensity
in the image, thereby spot is recognized. The objective function, which is implemented, helps in identifying
the exact pixel. The algorithm is applied to different sizes of sub images and features of the spots are
obtained. It is found that there is a tradeoff between accuracy in the number of spots identified and time
required for processing the image. Segmentation process is independent of shape, size and location of the
spots. Background estimation is one step process as both foreground and complete spot are realized.
Coding of the proposed algorithm is developed in MATLAB-7 and applied to cDNA microarray images.
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LiDAR Data Processing and ClassificationMichal Bularz
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The document discusses geospatial information technologies and geo-processing techniques. It provides an overview of geographic information systems (GIS), describing what GIS is, the types of questions it can answer, and its components like remote sensing, GNSS, and databases. It also covers vector and raster data models, projections and accuracy, and different geo-processing techniques for analysis like overlay operations, map algebra, and modeling. Big data challenges with geospatial data are also mentioned.
This document discusses geospatial information technologies and geo-processing techniques. It provides an overview of geographic information systems (GIS) and describes how GIS integrates technologies like remote sensing, cartography, GPS, and databases. It discusses vector and raster data models for storing spatial data and describes common geo-processing tasks for analyzing and modeling spatial data, including overlay operations and map algebra. Big data challenges in handling large, complex geospatial datasets are also mentioned.
digitalcartography in gis-200627114438 (1).pdfAshwini Rao
Digital cartography involves the generation, storage, and editing of maps using computers. It has advantages over analog cartography like easier updating and access to more users. Data is collected through remote sensing, aerial photography, GPS, or scanning and digitizing existing maps. This data is stored in digital databases that allow for spatial and non-spatial data. Users can then manipulate and analyze the data using GIS software to produce cartographic representations and support decision making.
Digital cartography involves the generation, storage, and editing of maps using computers. It has advantages over analog cartography like easier storage, updating, and access to data. Data is collected through remote sensing, aerial photography, scanning, and digitizing. GPS is also used. Digital databases store spatial and non-spatial data. Analysis and representation of data is facilitated using GIS tools. Digital cartography has made mapping accessible to non-specialists.
LIDAR uses laser pulses to measure distance and can be used to create digital elevation models (DEMs) and terrain models. LIDAR systems consist of a laser scanner, direct georeferencing system (GPS and INS), and computer processing. LIDAR data provides highly accurate elevation data that has many applications including flood inundation mapping, as demonstrated after Hurricane Katrina where LIDAR data helped assess flooding in New Orleans. LIDAR has revolutionized data collection and applications in mapping, engineering, and design through 3D modeling of terrain and structures.
Digital Elevation Model (DEM) is the digital representation of the land surface elevation with respect to any reference datum. DEM is frequently used to refer to any digital representation of a topographic surface. DEM is the simplest form of digital representation of topography. GIS applications depend mainly on DEMs, today.
The document provides an introduction to geographic information systems (GIS) and remote sensing. It discusses how GIS organizes and analyzes spatial data through data management, analysis, and visualization. It describes different data types including vector, raster, and imagery data. It also explains key concepts such as layers, modeling geospatial reality, and coding vector and raster data. The document outlines advantages and disadvantages of vector and raster data models. It introduces remote sensing and describes platforms and sensors used to collect spatial data from aircraft and satellites.
Surveyors already have access to ground-based, manned flight, and satellite data, so will they embrace this new technology in earnest?
By Bill McNeil, Contributor/Advisor, and Colin Snow, CEO and Founder, Skylogic Research, LLC
Goal location prediction based on deep learning using RGB-D camerajournalBEEI
In the navigation system, the desired destination position plays an essential role since the path planning algorithms takes a current location and goal location as inputs as well as the map of the surrounding environment. The generated path from path planning algorithm is used to guide a user to his final destination. This paper presents a proposed algorithm based on RGB-D camera to predict the goal coordinates in 2D occupancy grid map for visually impaired people navigation system. In recent years, deep learning methods have been used in many object detection tasks. So, the object detection method based on convolution neural network method is adopted in the proposed algorithm. The measuring distance between the current position of a sensor and the detected object depends on the depth data that is acquired from RGB-D camera. Both of the object detected coordinates and depth data has been integrated to get an accurate goal location in a 2D map. This proposed algorithm has been tested on various real-time scenarios. The experiments results indicate to the effectiveness of the proposed algorithm.
LocalizationandMappingforAutonomousNavigationin OutdoorTerrains: A StereoVisi...Minh Quan Nguyen
The document discusses localization and mapping techniques for autonomous navigation using stereo vision. It describes using efficient stereo algorithms to build local maps, visual odometry for precise registration of robot motion and maps, and integrating local maps into a globally consistent map. The approach is tested in outdoor environments, where it is able to build accurate maps in real-time and outperform other teams in validation tests.
The document discusses the validity of the argument that off-the-shelf 3D GIS software cannot model Hong Kong's complex 3D cityscape. It outlines Hong Kong's dense, irregular urban form with tall, uniquely shaped buildings. It also describes 3D modeling and visualization processes, including data acquisition methods like LIDAR, and capabilities of various 3D GIS software packages. While software allows complex 3D modeling, limitations include inability to access interiors and fully represent Hong Kong's unique urban landscape.
The document provides an overview of photogrammetry, which is the science and technology of obtaining reliable spatial information about physical objects and the environment through analyzing photographs. It discusses the different types of photogrammetry including aerial/spaceborne photogrammetry and close-range photogrammetry. It also summarizes the key techniques, applications, and products of photogrammetry such as digital terrain models, orthophotos, and 3D models.
This document proposes and evaluates several deep learning models for unsupervised monocular depth estimation. It begins with background on depth estimation methods and a literature review of recent work. Four depth estimation architectures are then described: EfficientNet-B7, EfficientNet-B3, DenseNet121, and DenseNet161. These models use an encoder-decoder structure with skip connections. An unsupervised loss function is adopted that combines appearance matching, disparity smoothness, and left-right consistency losses. The models are trained on the KITTI dataset and evaluated using standard KITTI metrics, showing improved performance over baseline methods using less training data and lower input resolution.
The document discusses quantifying uncertainty in digital terrain models (DTMs) used in geographic information systems (GIS). It compares several interpolation methods for creating DTMs from point data, including inverse distance weighting, spline interpolation, and Kriging. It also examines using remote sensing image data to automatically generate dense point clouds for DTMs. The author tests eight interpolation algorithms on real elevation data from Oradea, Romania to create DTMs and evaluates their quality using statistical measures like root mean square error. While B-cubic interpolation had the lowest RMSE, visual analysis found Delaunay triangulation and Shepard interpolation most closely matched the reference DTM, showing limitations of relying solely on statistics for evaluation. The paper aims to establish parameters
In order to prevent heritage and important sites, an inspection was carried out to identify different tools and techniques for 3D modeling. It was observed that a good quality work is done all over the world, related to 3D modeling. Different tools and techniques where adopted by different researchers along which different data acquisition were used. It was studied that different tools are suitable to work with different type of 3D model generation.
From Sense to Print: Towards Automatic 3D Printing from 3D Sensing Devicestoukaigi
The document describes a system called "From Sense to Print" that automatically generates 3D printed models from 3D sensor data without manual intervention. The system uses a Kinect sensor to reconstruct objects, KinectFusion for reconstruction, and sends the resulting 3D models to a 3D printer. It proposes a semantic segmentation algorithm to process the reconstructed data into a printable form by scaling it to the printer size. Initial results from a prototype using these components are presented along with limitations of the current approach.
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Immersive 3 d visualization of remote sensing data
1. Signal & Image Processing : An International Journal (SIPIJ) Vol.4, No.5, October 2013
IMMERSIVE 3D VISUALIZATION OF REMOTE
SENSING DATA
1
Surbhi Rautji, 2Deepak Gaur, 3Karan Khare
1
2
Oracle Technologies, Oracle 3C Building, Sector 127, Noida (UP), India
Department of Computer Science and Engineering, AMITY University, Noida, India.
3
Gradestack Pvt. Ltd, TLABS, Times Tower, Noida (UP), India
ABSTRACT
Immersive 3D Visualization is a java based Engine for viewing the Data of aerial images in the Three
Dimensional with provision for simulation and fly through. This application is based on java Technology
and works as standalone application, applet or in the browser. Immersive 3D Visualization is a good
application to use where the area of interest is to view the interested regions rather than the big images.
The Immersive 3D Visualization is a application that is designed on Java 3D Technology and Open
Graphics Libraries. The Java3D Technology of envisages the three Dimensional view of the picture and
Open Graphics Libraries allows one to view the rendered data on to the screen. This visualization
application is java based hence no portability issues. The Data for this work is collected from the various
sites like Google earth, USGS web site for the data viewing. This work takes an advantage of modelling the
3D DEM data on the visualized portion of the screen, and thus this approach is optimized. We are
assumed that the DEM is coming from the other source or it is simulated Data in order to view the threedimensional pictures. The Process of Collecting the Data and projecting on to the screen is done by
collectively Open Graphics Libraries and Java 3D Technology. In this work any image can be viewed as
3D with the use of DEM data, which can be created with use of certain selected values, where as in the
case of Google Earth it is not possible to see your own image in 3D. The work done here can be used for
selected region of interest, unlike Google Earth, which is used for continuous streaming of data. Our work
on 3D immersive visualisation can be useful to GIS analyst to view their own images in 3D.
KEYWORDS
3D Visualisation, Immersive, Image Representation, Indexed Color Mode, JAVA 2D, JAVA 3D, OpenGL
1. INTRODUCTION
With the rising number of sensors carried on board of planes and satellites and the increasing
performance of these sensors new tasks originates, which are handled more efficiently by remote
sensing techniques than by standard procedures. These tasks include data acquisition for
geographic information system, refinement of data using higher level of details like DEM, update
and verification of existing GIS database.
Software systems in yesteryears have gone through a great evolution and have allowed various
highly developed techniques to facilitate the work spheres. One such significant development is
generation of 3D city models.Still there is a continuing need for an improvement of these systems
to enable a further reduction of human interaction, with an idea of fully automatic systems.
However, it is becoming even more important to develop newer and more updated and efficient
applications and tools to view and manage the acquired 3D city models. This paper is an example
which gives an overview of the latest updates on generation and visualization of 3D site models.
DOI : 10.5121/sipij.2013.4505
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2. Signal & Image Processing : An International Journal (SIPIJ) Vol.4, No.5, October 2013
A 3D site model generally includes a description of the terrain, of the streets, buildings and
vegetation of the concerned built-up area. Although for several applications additional
information is required, mainly buildings and man-made objects are important factors of a 3D site
model. For instance, a realistic representation of such a virtual reality applications can be
acquired only when the texture of the ground, roofs and facades is available as well as other
significant details such as trees, footpaths and fences.
The purpose of this given work is technology of 3D-site model generation in PC-level computers.
In this technology digital terrain models on large territory are created using space images, and
man made objects and terrain models containing them are created using large scale aerial images.
1.1 Image Representation
The data that is acquired from various sources like satellites, aerial based images are of two
dimensional in nature. These two dimensional image is a raster image where in it consists of
pixels. Pixel is considered as a smallest element. The image is formed with the pixels. The data
that is acquired from various aerial image will have the two dimensional representation of the
image.
1.2 Two dimensional picture of an image
The image which was shown above is the two dimensional picture of the image. The image has
gray values that are shown above. The image will have different colors. The various colors of the
image are RGB – Red Green Blue, Indexed Color Mode. The image can be of 8 bit, 16 bit or 24
bit image. 8 bit image occupies the 1 byte, 16 bits is 2 bytes and 24 bit image is 3 bytes.
1.3 Three dimensional picture of an image
In order to represent the Three dimensional picture of the image, one needs the two dimensional
picture of the image and a height information that is constructed with the elevation of the image.
The elevation of the image is being constructed with the models Digital Elevation models and
Digital Terrain Models. In the DEM the elevation is computed without surface information but in
DTM surface models are taken into account.
1.4 Remotely Sensed Satellite Imagery
Remote sensing is the acquisition of information about an object or phenomenon, without making
physical contact with the object. In contemporary parlance, the remote sensing generally refers to
the use of aerial sensor technologies to detect and classify objects on Earth (both on the surface,
as well as in the atmosphere and oceans) through the transmitted signals (e.g. electromagnetic
radiation emitted from aircrafts or satellites).
These satellite images serve many purposes in the fields of meteorology, agriculture, geology,
forestry, landscape, biodiversity conservation, regional planning, education, intelligence and
warfare.
There exist two key categories of remote sensing: Passive Remote Sensing and Active Remote
Sensing. The Passive Sensors are able to detect natural radiation that emit or reflect by the object
or the surrounding area that is being observed. The most common source of radiation that passive
sensors measure is Reflected Sunlight. Some examples of passive remote sensors are film
photography, infrared charge-coupled devices and radiometers.
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3. Signal & Image Processing : An International Journal (SIPIJ) Vol.4, No.5, October 2013
Entirely opposed to that, Active Remote Sensing emit energy that scan objects and areas and then
a sensor detects and measures the radiation reflected or backscattered from the target. RADAR is
one of the examples of Active Remote Sensing in which the time spent between the emission and
return is measured, which establishes location, height, speed and direction of an object.
The Remote Sensing technology has allowed man to acquire data on and from hazardous and
inaccessible areas. Remote sensing applications have been used to monitor deforestation in areas
such as the Amazon Basin, glacial features in Arctic and Antarctic regions, and collecting
information by means of waves (possibly sound) in coastal and ocean depths. During the cold
war, the data collected ta about dangerous border areas was used by the Military. Remote sensing
also replaces costly and slow data collection on the ground, ensuring in the process that areas or
objects are not disturbed.
The quality of remote sensing data depends on its spatial, spectral, radiometric and temporal
resolutions. The size of a pixel recorded in a raster image is called Spatial Resolution. The pixels
match up to the square area which ranges in side length from 1 to 1,000 metres (3.3 to 3,300 ft).
The wavelength width of the different frequency bands recorded is called Spectral Resolution.
Generally, this is related to the number of frequency bands that the platform records.
The number of different intensities of radiation that the sensor is able to differentiate between is
called Radiometric Resolution. Usually, its range is between 8 to 14 bits, which corresponds to
256 levels of the gray scale and is up to 16,384 intensities or "shades" of colour, in each of the
band. It also depends on the instrument noise.
1.5 Digital Elevation Model
A digital representation of ground surface topography or terrain is called its digital elevation
model (DEM). Generally, it is also known as a digital terrain model (DTM). A DEM can be
symbolised by a raster (a grid of squares) or by a triangular irregular network. Frequently DEMs
are constructed using remote sensing techniques, but, they may also be made from land survey.
Geographic Information Systems use DEMs quite often and are the most regularly used for
digitally creating relief maps.
The data preparation for this work depends on the selection of the raster image and its selection of
DEM Data. The DEM data is also raster data. This Data will give the height information. Data
preparation model for this work depends on the proper selection of the Digital Elevation Model
for the image.
In Order to find the correct height information value one should study about the Digital Elevation
model generation. The height information for the Raster images is generated using the satellite
imageries. There are various mathematical modelling techniques are available for the DEM
generation.
DEMs may be created in a several ways, but often they are acquired by Remote Sensing rather
than using direct survey. Interferometric Synthetic Aperture Radar is one of the dominant
techniques for generating digital elevation models. Two passes of a radar satellite (for example
RADARSAT-1) are required to generate a digital elevation map for converting from a resolution
of ten meters through ten kilometres. One can also acquire an image of the surface cover.
Digital image correlation method is also a very significant and powerful technique for creating a
Digital Elevation Model. It involves two optical images obtained with different angles that have
been taken from the same pass of an airplane or an Earth Observation Satellite (for example the
HRS instrument of SPOT5).
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Former methods of creating DEMs generally involve interpolating digital contour maps that could
have been generated by direct survey of the land surface; the method is still being used in
mountain areas where superimposition of waves or interferometry is not always as desired. It is
important to keep in mind that the contour data or any other sampled elevation datasets (by either
GPS or ground survey) do not fall under the category of DEMs, they can be accepted as Digital
Terrain Models. A DEM signifies the continuous availability of elevation at each location in the
area of the study.
The quality of a DEM represents the accuracy of elevation at each pixel (absolute accuracy) and
the accuracy of morphology presented (relative accuracy). The quality of DEM derived products
depend on various factors that such as roughness of the terrain, sampling density (elevation data
collection method), grid resolution or pixel size, interpolation algorithm, vertical resolution and
terrain analysis algorithm. The common uses of DEMs include creation of relief maps, modeling
water flow or mass movement (for example avalanches), extracting terrain parameters, rendering
of 3D visualizations, creation of physical models (including raised relief maps), rectification of
aerial photography or satellite imagery, reduction (terrain correction) of gravity measurements
(gravimetry, physical geodesy), terrain analyses in geomorphology and physical geography.
1.6 OpenSceneGraph
For development of high performance graphics applications viz. flight simulators, games, virtual
reality and scientific visualization, the OpenSceneGraph is an Open Source library and crossplatform graphics toolkit. It is based on the concept of a SceneGraph, which provides an object
oriented framework on top of OpenGL. This gives the developer the scope to have many
additional utilities for a fast development of graphics applications as well as sets the developer
free from implementing and optimizing low level graphics calls.
1.6.1 Features
The main objective of the OpenSceneGraph is to make the benefits of scene graph technology
freely available to everyone, including commercial and non-commercial users. The program is
written completely in Standard C++ and OpenGL, and it fully utilizes the STL and Design
Patterns. It also capitalizes on the open source development model to provide a development
library that is legacy free and focuses on the needs of end users.
The principal strength of OpenSceneGraph is its performance which includes supporting viewfrustum culling, occlusion culling, small feature culling, Level of Detail (LOD) nodes, OpenGL
state sorting, vertex arrays, vertex buffer objects, customization of the drawing process, and
OpenGL Shader
Language and display listsis one feature of core scene graph while Productivity is the feauture of
core scene graph that lets encapsulates the majority of OpenGL functionality including the latest
extensions, provides rendering optimizations features such as culling and sorting, and set of addons generally libraries which makes it possible to develop high performance graphics applications
very rapidly, scalability with the scene graph is that it will not only run on portables all the way
up to high end multi-core, multi-GPU systems and cluster, portability of core scene graph has
been designed to have minimal dependency on any specific platform.
2. RELATED WORK
Interest in 3D site models has risen significantly in the past years. Originally, it was considered
one of the main application areas to simulate the circulation of electromagnetic waves. These are
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used by network operators for the planning of antenna locations. Although, it is quite likely that
there may be further need in the coming times due to the approaching introduction of UMTS
networks in Europe, there are other areas evolving as well, such as 3D navigation systems and
visualization for the city and building planning or architectural contests.
Geographic Information System (GIS) are widely accepted tools for geodata visualization and
analysis. Many GIS application suffer from the lack of basic geospatial data. As acquiring
geodata is quite time intensive and resource intensive methods for automated object recognition
in raster images are indisputably beneficial and therefore of interest for any GIS application.
Along with this point of view goes the effect that the basic functionality of a GIS is not only
visualization but also interaction and analysis on geospatial objects in connection with highly
sophisticated scientific visualization methods for large datasets. Many approaches simply cover
2D aspects only (common GIS approach) or are designed to support 3D data output. But, recently
more and more GIS are combined with virtual reality environment, taking into account the GIS
typical data analysis and decision support functionality, which has to involve highly sophisticated
3D interaction facilities.
Common to all these GIS applications is that they suffer from the lack of suitable set of basic
geospatial data. Especially in applications which involve decision support in urban planning, upto-date information about buildings, traffic infrastructure and other man made features.
The difference that exists between this work and Google Earth is that, Firstly; Google Earth
requires only its own specific height information data and cannot use different height information
while in our work this is no limitation in our work since any height data can be used i.e. Digital
Elevation Modal. Secondly in our work any image can be used to get its 3D output with the use of
DEM data while in the case of Google earth not every image can be used to produce the 3D
output.
3. CLASSIFICATION IN 3D IMMERSIVE VISUALIZATION
3.1 JAVA 2D Programming
Java 2D is a client side graphics programming. The Java 2D programming is meant for two
dimensional graphics representations. The various forms in which this programming is done is
either Frame based applications which can be run as a standalone applications and Applet Based
applications which can be run in the browser.
The advantages of Java 2D programming is of its ease of use and java’s platform and
architectural neutral. An application programming Interface (API) was developed at Sun Micro
Systems called Java 3D for rendering interactive 3D graphics employing Java Programming
language. Java 3D is a client side Java API and it includes the abstract Windows Toolkit (AWT)
and Java Foundation classes(JFC/Swing), which are both java class libraries for creating
applications with a Graphical User Interface(GUI).
Java 3D depends on OpenGL or DirectX for its functioning of native rendering, whereas the 3D
scene description, application logic, and scene interactions are located in Java code. When Sun
decided to design Java 3D, although they did not have the required means and resources or the
support from the industry to substitute OpenGL, they still wanted to pull more of its strengths as
an object oriented programming (OOP) language in place of merely entrusting it to a procedural
language such as C. Whereas OpenGL’s description for a 3D scene comprises of points, lines and
abstraction, Java 3D is able to describe a scene as a collection of Objects. In raising the level of
description and abstraction, Sun applied OOP principles to the graphics domain on one hand and
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on the other, also brought in scene optimizations that can make up for the involvement and
increasing the overhead by means of calling through JNI.
3.2 JAVA 3D API
The Java 3D API is a ladder of Java classes which act as the interface to a refined three
dimensional graphics rendering and sound rendering system. The programmer employs high-level
concepts for creating and manipulating 3D geometric objects. These geometric objects are located
in a virtual space, which is then rendered. The API is planned with the elasticity to create precise
virtual spaces of a great variety of sizes from astronomical to subatomic.
A Java 3D program produces instances of Java 3D objects and situates them into a scene graph
data structure. The scene graph is an arrangement of 3D objects in a tree structure that fully
specifies the content of a virtual space, and how it is to be rendered.
3.3 Building a Scene Graph
A Java 3D virtual space is created from a scene graph. A scene graph is produced using instances
of Java 3D classes. The scene graph is put together from objects to define the geometry, sound,
lights, location, orientation, and appearance of visual and audio objects.
A common definition of a graph is a data structure composed of nodes and arcs. A node is a data
element, while arc is the relationship between data elements. The nodes in the scene graph are the
instances of Java 3D classes. The arcs represent the two kinds of relationships between the Java
3D instances.
The most universal relationship is a parent-child relationship. A group node can have any number
of children but only one parent, whereas leaf node can have one parent and no children. The other
relationship is a reference. A reference associates a Node Component object with a scene graph
Node. The geometry and appearance attributes used to render the visual objects are defined by the
Node Component objects.
A Java 3D scene graph is built of Node objects in parent-child relationship in a tree structure. In a
tree structure, one node is the root. Other nodes are accessible following arcs from the root. The
arcs of a tree form no cycles. A scene graph is formed from the trees rooted at the Locale objects.
The Node Components and reference arcs are not part of the scene graph tree.
In a tree, there is only one passageway from the root of a tree to each of its leaves; similarly, there
is only one passageway from the root of a scene graph to each leaf node. The passageway from
the root of a scene graph to a specific leaf node is called the leaf node’s scene graph path. Since a
scene graph path leads to exactly and only one leaf, there exists one scene graph path for each leaf
in the scene graph.
Each scene graph path in a Java 3D scene graph specifies fully the state information of its leaf.
State information includes the following: location, orientation, and size of a visual object. As a
result, the visual attributes of each visual object depend only on its scene graph path. The Java 3D
renderer capitalizes on this fact and renders the leaves in the order it considers to be most
efficient. The Java 3D programmer usually does not have control over the rendering order of
objects.
To design a Java 3D virtual space a scene graph is made using the standard set of symbols. After
the design has been completed, that scene graph drawing is the specification for the program.
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After the program is complete, the same scene graph is a concise representation of the program.
The scene graph can be created through a program which can be generated using an existing
scene graph. A scene graph drawn from an existing program document the scene graph the
program creates.
3.4 JAVA 3D Advantages and Strengths
Java 3D allows the programmers to program in 100 percent java code. In any significant 3D
application, the rendering code composes only a part of the total application. It is very good to
have the application code, persistence, and user interface (UI) code in an easily portable language,
such as Java. Although Sun’s promise of Write-Once-Run-Anywhere was considered more to be
marketing strategy than a realistic claim. Especially for the client side programming, java has
done significant work to enable development of applications that can be easily moved between
platforms. The platforms of a wide range of interest today are Microsoft Windows 98/NT/2000,
Sun Solaris, LINUX, and Macintosh OS X.
Many of the latest 3D graphics applications being developed with java 3D today are capitalizing
on the strengths of java as the language for the internet.
Java 3D includes a view representation designed for use with head-mounted displays (HMDs) and
screen projectors. By protecting the programmer from much of the complex trigonometry
required for such devices, java 3D simplifies the transition from a screen centric rendering model
to a projected model, where rendering in stereo allows for greater practicality.
Java 3D also includes in-built support for sampling 3D input devices and rendering 3D spatial
sound. By combining the above elements into a unified API, java 3D has achieved a design
uniformity that few other APIs can claim.
Java 3D’s higher level of concept of rendering the scene also has the scope of interactive 3D
graphics for a new class of audience people who would typically have been considered 3D
content creators. It may appear the 3D graphics as a spectrum, with functional resources and
capabilities distributed across a variety of tasks.
3.5 Limitations of Java 3D
There are some OpenGL features that are difficult or not possible to achieve within Java3D for
some programmers coming from OpenGL. Some of them may actually miss the total control they
normally have over the scene and the rendering process. Many others, however, quickly learn
mapping from OpenGL functions to java 3D Objects and even appreciate the output gains they
can have using java 3D.
A skilled developer using OpenGL and native C code will be able to achieve higher performance
than a Java Programmer who uses Java 3D despite the fact that java 3D includes some clever
optimizations. However, if absolute rendering performance is the top-main concern of the
application then it would be advisable to use OpenGL or another native rendering API.
4. DESIGN AND IMPLEMENTATION
3D lets users view regularly gridded DEM data as a continuous topographic surface with colored
contours, shading, or overlaid physical, cultural or environmental data. The program facilitates
for the users viewing of the texture of the gridded surface and the variation of relief. The map
area can be examined with different light sources, from varying directions and heights above the
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horizon. It is possible to rotate the maps 360 degrees around a vertical axis and 90 degrees
around a horizontal axis. Map projections can be exaggerated or zoomed.
Our work has been implements and designed on Java 3D Technology. The implementation works
on Java 3D and uses the Open Graphics Libraries for visualization.
Key events
APPLICATION SCENE GRAPH
Java 3D
Mouse events
OGL
Operating System
Hardware
Graphics
Fig: 1 - Architecture of the application
4.1 3D Terrain Visualization with Flythrough Scenes
To appraise the condition of the terrain in areas whether remote or urban , to detect any potential
operational problems, and to ensure that radio communication and telemetry signals are
maintained, DEMs and high-resolution Orthorectified Satellite Images are employed to generate a
representation of the local terrain conditions. One can manage all this without having to step out
of one’s office.
Satellite Imaging Corporation is able to provide its customers with 5m digital surface models
(DSMs), 2m digital elevation models (DEMs), and a detailed orthorectified satellite image mosaic
at 0.8m resolution by utilizing stereo IKONOS satellite image data.
DEMs can be generated from the HRS SPOT-5 satellite sensor with a resolution of 20m and with
a resolution of 15m from the stereo ASTER satellite sensor. Explorers and operators can achieve
a substantial improvement in the visualization of surface and exploration targets by this data that
provides 3D imagery.
The benefits are quite evident, such as in the field of oil and gas exploration. A combination of
the described process and the interpreted seismic data in the vertical dimension can provide a
greater understanding of the relationship between surface and subsurface structures at both basin
and prospect scale. When viewed in an immersive 3D visualization environment, this
understanding is more enhanced. These services are exceptionally helpful for project planners,
operation managers, and logistics managers in order to plan field operations in a computer
environment, ensuring the best access and project objectives’ achievement for engineering and
construction of pipeline, transmission, road design, oil and gas exploration and production
activities, 2D/3D seismic data acquisition, environmental studies, motion pictures, aviation,
media and so on.
High resolution geometrically corrected Satellite Image Data has to be available or obtained from
the 0.62m QuickBird, 0.81m IKONOS or 2.5m SPOT-5 satellite sensors so as to provide 3D
Terrain Modeling and visualization capabilities in a GIS or mapping computer environment. In
certain instances, use of digital orthoaerial photography is also possible.
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Availability of an accurate digital terrain model (DTM) is a must if a 3D terrain modeling and
visualization application is expected to have good details about the terrain features and terrainslopes to facilitate critical project decisions. If, in the Area of Interest (AoIs), there is no
appropriate DTM available then a DTM can be obtained by certain satellite and aerial sensors
such as LiDAR (Light Detection and Ranging), Stereo Aerial Photography, IFSAR
(Interferometric Synthetic Aperture Radar) and Stereo High Resolution Satellite Image Data.
These remote sensors have been listed in an orderly manner based on their resolution, capability
and accuracy.
4.2 Image Data or Texture Data
The data that is to be projected on the application is given by the Image Data or Texture Data.
Sample Data Sets of the Image or Texture Data is shown below
Fig: 2- Texture Sample 1
Fig: 3- Texture Sample 2
The Simulated Digital Elevation data is shown in the below picture.
Fig: 4- Simulation Digital Elevation data
After the Data is prepared, the next process involves the draping and rendering process.
The Diagram of the draping the Texture image on the Elevation is shown below.
Rendering
SCREEN
Texture
Load
Actor
Height
Info Read
Fig: 5- Draping the Texture image on the Elevation
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4.3 Process Diagram
The diagram explains the step by step of each sequence taking place in the system. The sequence
diagram for the work implementation is shown below
Data Preparation
user
start
Texture Load
Height Load
Scene Graph
Actions Events
Texture Data
If success
Height
If failure
If success
Event
If failure
Rendered
Scene
update
Fig: 6- Process Diagram
The 3D Visualization tool follows a sequence of steps for data representation starting with the
Data Preparation where in the data sets are collected then the collected data sets are Texture Data
and DEM (Digital Elevation Model) Data. The texture Data will give the image information and
the Height information is given by the elevation data. This elevation information forms the mesh.
The data after preparation will be given to the scene Graph. The Scene Graph is the Open
Graphics Library function that calls the method to render the graph on to the screen. The various
events are attached to the scene Graph. The events are of two types: Keyboard events and Mouse
Events. Based on the selection of events the scene graph gets updated on to the screen. The scene
graph is update continuously based on the selection of events.
5. DISCUSSION
The result of this application created is 3D visualization of satellite image. For generating a 3D satellite
image, first the data is prepared where in the data sets are collected. The collected data sets are
Texture data and DEM (Digital Elevation Model) data. Texture data will give the image
information and DEM data will give the height information. Now we render these two
information to generate a scene graph and thus the 3D visualization of a satellite image is
generated. This scene graph is generated using the concept of Java 3D. In this application work
we have implemented some functionality like forward, backward, left, right, etc.
By changing the YAW i.e. the z value in the co-ordinate system user changes the height
information. By reducing the height we are coming near to the object. In application we can
forward the satellite image by pressing ‘W’ Key. By increasing the height we are going away
from the object. In application we can Backward the satellite image by pressing ‘S’ Key. By
changing the ROLL i.e. the x value in the co-ordinate system users changes the x direction. Value
Decrease in x direction moves the image in the left direction. In application we can move satellite
image in left direction by pressing ‘a’ key. Value increase in x direction moves the image in the
right direction. In application we can move satellite image in right direction by pressing‘d’ key.
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Forward
Backward
Left
Right
Fig: 7 -3D Visualization of satellite image moving in various directions
Texture Rendering is a process of displaying the satellite image from the frame buffer to the
display. Frame buffer is the memory area assigned in the graphic card which controls the display
information on the screen. Each pixel on the screen occupied is memory location in the frame
buffer. In application we can see texture rendering process by pressing the numeric key ‘1’.
Fig: 8– Texture rendering of a Satellite image
To represent any feature properly we need to create a TIN (triangular irregular node) there we
create a mesh by joining a tin. To represent a surface we need minimum 3 points because 4 points
cannot represent a surface. DEM data is available in image at some intervals; let’s say DEM data
is available in image after every 100 pixels (100x100). We take a point (x, y) at a particular
interval and then take DEM information. Now we take these 3 points i.e. coordinates of x
direction, y direction and height information of each point to form a triangle. Same process is
repeated for the complete image. These triangles jointly describe as wired mesh. Wire mesh is
used to locate and identify any specific location which is difficult to pinpoint in texture rendered
satellite image display. In application we can see texture rendering process by pressing the
numeric key ‘2’.
Fig: 9 – Wire rendering of satellite image
In high resolution terrain/object is seen from a near distance we see a high resolution data. From
higher z value to lower z value we see more detail of data. In this application we can see high
resolution by pressing the numeric key ‘3’.In low resolution terrain/object is seen from a farther
distance we see a low resolution data. From lower z value to higher z value we see less detail of
data. In this application we can see high resolution by pressing the numeric key ‘4’.
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Fig: 10- High resolution of a Satellite image
Fig: 11- Low resolution of a Satellite image
6. CONCLUSION
We have presented geometrical and topological models for visualization of terrains and manmade features in satellite and aerial imagery. With an idea of visualization we have developed a
robust and optimised engine for the three dimensional view of the image. It is based on the Java
3D technology and uses open graphics libraries for data view and visualization. The
advantage of this work is it can be used for a selected portion of data for optimization. This
work is based on the java Technology hence no portability issues are involved. Generation of 3D
site models is being promoted commercially with main use in the private sector. However,
research activities on 3D site models which support the commercial activities are immature at
present, though several interesting research activities have been and are still performed.
7. FUTURE PROSPECTS
This model still have a lot of future to be accomplished i.e. Mouse Events Screen is to be added to
applet, mouse listeners are to be designed extensively and have to be tested, the option should be
given in the application to add different resolution of satellite images in the respective layer to
enhance the level of details, Performance Improvements are to be tested, the application is to be
tested for various data sets collected from Google earth, look and feel of the application is to be
improved, testing of the application is to be rigorously made.
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Authors
Surbhi Rautji is a Software Engineer in Oracle Technologies. She has completed her
Bachelor of Technology in Computer Science and Engineering from Amity University,
Noida, India. Her fields of interest include Image Processing, Image security, Artificial
Intelligence, Cognitive Studies, Genetic Algorithms and Computer Network Security.
Deepak Gaur had received Master of Engineering in Computer Science & Engineering
from Punjab Engineering college, University of Technology, Chandigarh. He has
completed his B.Tech in Computer Science & Engineering from Himachal Pradesh
University, Shimla(H.P). Presently he is working as Assistant Professor in CSE
Department, Amity University, Noida, Uttar Pradesh, India. Mr. Deepak Gaur, research
Area is Image Processing, Image Compression, Security Systems,Image Security and
pattern reorganization.
Karan Khare is a web developer and security specialist in Gradestack Pvt. Ltd. He has
completed his education in Bachelor of Technology in Computer Science and engineering
from Amity University, Noida. He holds interest in Artificial Intelligence, Genetic
Algorithms, Cognitive Studies, Computer Network Security and Image Processing.
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