Digital Image Processing is the manipulation of the digital data with the help of the computer hardware and software to produce digital maps in which specific information has been extracted and highlighted. Visual image interpretation techniques have certain disadvantages and may require extensive training and are labor intensive.
In this technique, the spectral characteristics are not always fully evaluated because of the limited ability of the eye to discern tonal value and analyze the spectral changes.
If the data are in digital mode, the remote sensing data can be analyzed using digital image processing techniques and such a data base can be used in Raster GIS.
In applications where spectral patterns are more informative, it is preferable to analyze digital data rather than pictorial data.
Satellite remote sensing data in general and digital data in particular have been used as basic inputs for the inventory and mapping of natural resources of the earth surface like forestry, soils, geology and agriculture.
Space borne remote sensing data suffer from a variety of radiometric, atmospheric and geometric errors, earth‟s rotation and so on.
These distortions would diminish the accuracy of the information extracted and reduce the utility of the data. So these errors required to be corrected.
it is highly useful for geography students in the field of remote sensing and it is in very simple and explanatory for the purpose of simplification with relevant images in this ppt.
Radiometric corrections include correcting the data for sensor irregularities and unwanted sensor or atmospheric noise, and converting the data so they accurately represent the reflected or emitted radiation measured by the sensor.
Raw remote sensing images contain errors that must be corrected through pre-processing before analysis. Pre-processing involves radiometric, geometric, and atmospheric corrections. Radiometric corrections address distortions in pixel values from issues like noise, striping, or dropped scan lines. Geometric corrections rectify distortions caused by terrain, sensor geometry, and platform movement using ground control points. Atmospheric corrections reduce haze effects through techniques like dark object subtraction that assume minimum surface reflectance values. Pre-processing is essential for producing accurate, georeferenced images suitable for analysis and interpretation.
The document provides an overview of thermal remote sensing. It discusses key concepts like the thermal infrared spectrum, atmospheric windows and absorption bands, fundamental radiation laws, thermal data acquisition using sensors, and applications in mapping forest fires, urban heat islands, volcanoes, and military purposes. Thermal remote sensing allows measuring the true temperature of objects and detecting features not visible in optical remote sensing. It has advantages like temperature measurement but maintaining sensors at low temperatures can be challenging.
Spatial interpolation techniques are used to estimate values at unsampled locations based on known sample points. There are two main types of interpolation methods: global methods which apply a single mathematical function to all points, and local methods which apply functions to subsets of points. Specific interpolation methods covered include Thiessen polygons, triangulated irregular networks, spatial moving average, trend surfaces, inverse distance weighting, and Kriging. Kriging is similar to inverse distance weighting but uses minimum variance to calculate weights.
This document provides an overview of geographic information system (GIS) analysis functions. It discusses several types of analysis that GIS is used for, including selection and measurement, overlay analysis, neighbourhood operations, and connectivity analysis. Overlay analysis allows for spatially interrelating multiple data layers and is one of the most important GIS functions. Neighbourhood operations consider characteristics of surrounding areas, such as through buffering or interpolation. Overall, the document outlines the key spatial analysis techniques that GIS provides for examining geographic data patterns and relationships.
This document discusses geodetic systems and how they represent the Earth mathematically. It defines key concepts like datums, ellipsoids, and coordinate systems. Specifically, it explains that datums define geodetic systems using reference ellipsoids that approximate the geoid and Earth's irregular shape. Common datums like NAD27, NAD83, and WGS84 are described that use different ellipsoids and reference points. It also outlines how latitude, longitude, and elevation are used in geographic coordinate systems to specify locations on Earth.
it is highly useful for geography students in the field of remote sensing and it is in very simple and explanatory for the purpose of simplification with relevant images in this ppt.
Radiometric corrections include correcting the data for sensor irregularities and unwanted sensor or atmospheric noise, and converting the data so they accurately represent the reflected or emitted radiation measured by the sensor.
Raw remote sensing images contain errors that must be corrected through pre-processing before analysis. Pre-processing involves radiometric, geometric, and atmospheric corrections. Radiometric corrections address distortions in pixel values from issues like noise, striping, or dropped scan lines. Geometric corrections rectify distortions caused by terrain, sensor geometry, and platform movement using ground control points. Atmospheric corrections reduce haze effects through techniques like dark object subtraction that assume minimum surface reflectance values. Pre-processing is essential for producing accurate, georeferenced images suitable for analysis and interpretation.
The document provides an overview of thermal remote sensing. It discusses key concepts like the thermal infrared spectrum, atmospheric windows and absorption bands, fundamental radiation laws, thermal data acquisition using sensors, and applications in mapping forest fires, urban heat islands, volcanoes, and military purposes. Thermal remote sensing allows measuring the true temperature of objects and detecting features not visible in optical remote sensing. It has advantages like temperature measurement but maintaining sensors at low temperatures can be challenging.
Spatial interpolation techniques are used to estimate values at unsampled locations based on known sample points. There are two main types of interpolation methods: global methods which apply a single mathematical function to all points, and local methods which apply functions to subsets of points. Specific interpolation methods covered include Thiessen polygons, triangulated irregular networks, spatial moving average, trend surfaces, inverse distance weighting, and Kriging. Kriging is similar to inverse distance weighting but uses minimum variance to calculate weights.
This document provides an overview of geographic information system (GIS) analysis functions. It discusses several types of analysis that GIS is used for, including selection and measurement, overlay analysis, neighbourhood operations, and connectivity analysis. Overlay analysis allows for spatially interrelating multiple data layers and is one of the most important GIS functions. Neighbourhood operations consider characteristics of surrounding areas, such as through buffering or interpolation. Overall, the document outlines the key spatial analysis techniques that GIS provides for examining geographic data patterns and relationships.
This document discusses geodetic systems and how they represent the Earth mathematically. It defines key concepts like datums, ellipsoids, and coordinate systems. Specifically, it explains that datums define geodetic systems using reference ellipsoids that approximate the geoid and Earth's irregular shape. Common datums like NAD27, NAD83, and WGS84 are described that use different ellipsoids and reference points. It also outlines how latitude, longitude, and elevation are used in geographic coordinate systems to specify locations on Earth.
This document discusses several approaches for atmospheric correction of remote sensing imagery:
1) Image-based methods like the dark pixel method and regression method estimate and remove atmospheric path radiance.
2) The empirical line method uses ground targets of known reflectance to model atmospheric effects.
3) Radiative transfer models precisely account for atmospheric conditions using numerical models like MODTRAN or 6S to convert pixel values to surface reflectance.
4) Relative correction methods normalize images without absolute calibration to surface reflectance. Atmospheric correction is needed to accurately analyze surface properties from remote sensing data and compare images acquired at different times or wavelengths.
Photogrammetry is the science of obtaining information about physical objects through photographs, without needing direct contact. It involves measuring and analyzing captured images. The name comes from Greek roots meaning "light", "drawing", and "to measure". Key developments included using photography for mapmaking in the 1840s-1850s, the photogrammetric stereoplotter in the 1890s, and aerial photography from balloons and planes in the 1860s-1900s, advancing the field into the digital era.
Urban Landuse/ Landcover change analysis using Remote Sensing and GISHarshvardhan Vashistha
This document provides an overview of land cover and land use change detection. It discusses techniques for detecting changes through analyzing satellite images over time. Methods include visual interpretation, image rationing, classification, and indices. Factors to consider include the objective, type of change to detect, and type of changes of interest, like land use, forest, or urban changes. Applications include monitoring land use change, deforestation, fires, wetlands, urban growth, and environmental changes. Proper selection of methods and data depends on the scale and specifics of the changes being examined.
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.
this presentation briefly describes the digital image processing and its various procedures and techniques which include image correction or rectification with remote sensing data/ images. it also contains various image classification techniques.
This document discusses methods for enhancing spatial features in images. It describes spatial filtering and convolution, which involve applying kernels or filters to images to emphasize different spatial frequencies. Low-pass filters smooth images by reducing high spatial frequencies related to fine detail, while high-pass filters have the opposite effect of sharpening images. Edge enhancement filters aim to highlight linear features by increasing contrast around edges. Fourier analysis represents images as combinations of sine and cosine waves of different frequencies and can reveal features aligned in various directions.
This document discusses remote sensing and geographical information systems in civil engineering. It covers various topics related to remote sensing sensors including optical sensors, thermal scanners, multispectral sensors, passive and active sensors, scanning and non-scanning sensors, imaging and non-imaging sensors, and the different types of resolutions including spatial, spectral, radiometric, and temporal resolution. It provides examples and illustrations of these concepts.
This document provides an overview of remote sensing basics. It defines remote sensing as acquiring information about an object without direct contact. It discusses key elements of the remote sensing process including energy sources, atmospheric interactions, data acquisition by sensors, and data analysis. It also covers topics like the electromagnetic spectrum, atmospheric scattering and absorption, atmospheric windows, and spectral signatures. The document is intended as an introduction to fundamental concepts in remote sensing.
Data models are a set of rules and/or constructs used to describe and represent aspects of the real world in a computer. GIS can handle four data models for various applications. This module explains those four.
This document discusses different types of GIS data. Spatial data represents geographic locations and features on Earth and includes data types like points, lines, and polygons. Attribute data describes characteristics of spatial features like forests stands and includes data types like tabular data. Raster data models land cover with square grid cells, while vector data represents features as points, lines, or polygons which can accurately show shape and topology. Spatial data is mapped and stored with coordinates, while attribute data describes characteristics and is often linked to spatial data in a database.
This document provides an overview of ground truthing for remote sensing. It defines ground truth as observations or measurements made near the Earth's surface to support air or space-based remote sensing. Ground truth is collected using tools like GPS, radiometers, cameras, and topographic maps. It involves field observations, spectral measurements, location coordinates and sample collection to validate remote sensing data and reduce classification errors. The process of ground truthing helps verify pixel contents on satellite images and assess classification accuracy.
Image quality assessment and statistical evaluationDocumentStory
This document discusses image quality assessment and statistical evaluation of remote sensing data. It explains that remote sensing data can contain errors introduced by the environment, sensor issues, or processing errors. Assessing image quality and statistical characteristics is important and can be done by examining histograms, pixel values, and univariate and multivariate statistics to identify anomalies or redundancy in the data. The document also provides mathematical notation for describing remote sensing data and discusses concepts like populations, samples, sampling error, and the importance of metadata for image analysis.
This document discusses different types of map projections used to represent the spherical earth on a flat surface. It describes how all projections involve some distortion of properties like shapes, areas, distances or directions. The key types are conformal, equivalent, and equidistant projections. It explains the concepts of projection surfaces like cones, cylinders and planes, as well as variables like the light source and orientation. Specific common projections are also outlined, such as Mercator, Lambert conformal conic, and azimuthal equidistant, along with their characteristic distortions and uses.
10-Image rectification and restoration.pptAJAYMALIK97
This document discusses digital image processing. It defines digital image processing as the computer-based manipulation and interpretation of digital images. It outlines seven broad types of computer-assisted operations, including image rectification and restoration, image enhancement, image classification, data merging and GIS integration, hyperspectral image analysis, biophysical modeling, and image transmission and compression. It provides details on image rectification and restoration, which involves preprocessing to correct distorted or degraded image data through techniques like geometric distortions, radiometric calibration, and noise elimination.
Types of Platforms
1. Airbrone Platforms
2. Spacebrone Platforms
Platforms are Vital Role in remote sensing data acquisition
Necessary to correct the position the remote sensors that collect data from the objects of interest
Geo synchronous and Sun synchronous SatellitesTilok Chetri
There are three main types of satellite orbits:
1) Polar orbits have an inclination of 90 degrees, allowing satellites to observe the entire Earth as it rotates. They complete an orbit every 90 minutes.
2) Sun synchronous orbits allow satellites to pass over the same location at the same local time each day. These orbits are between 700-800 km in altitude.
3) Geosynchronous orbits circle the Earth at the same rate it rotates, allowing satellites to continuously observe nearly half of the Earth. These orbits are used for weather monitoring and communication satellites. Each orbit type has advantages and disadvantages for different applications.
Galileo used early optical enhancements in the 1600s to observe celestial bodies and merchant ships arriving in harbor. In the 1880s, Arthur Batut affixed cameras to kites, including an altimeter to determine the scale of images, making him the father of kite aerial photography. By 1903, camera miniaturization allowed cameras to be attached to pigeons for aerial photography, though images were limited. During the Cuban Missile Crisis in 1962, U-2 spy planes detected Soviet nuclear missiles in Cuba using remote sensing, changing the course of history.
IMAGE INTERPRETATION TECHNIQUES of surveyKaran Patel
Image interpretation is the process of examining an aerial photo or digital remote sensing image and manually identifying the features in that image. This method can be highly reliable and a wide variety of features can be identified, such as riparian vegetation type and condition, and anthropogenic features
This document discusses three methods for measuring height from aerial photographs: relief displacement, shadow length, and stereoscopic parallax. Relief displacement measures height by how far an object is shifted from its true position in an aerial photo due to its elevation. Shadow length measures height by using the length of an object's shadow and the sun angle. Stereoscopic parallax measures height by comparing the difference in an object's position between two overlapping aerial photos taken from different positions. Formulas are provided for calculating height from measurements obtained using each of these three methods.
This document provides an overview of digital image processing techniques. It defines digital image processing as the manipulation of digital image data using computer hardware and software. The document outlines several key stages in digital image processing, including pre-processing, image enhancement, image filtering, image transformation, and image classification. Pre-processing involves correcting geometric distortions and radiometric errors in remote sensing data. Image enhancement improves image quality. Image filtering separates images into spatial frequencies. Image transformation converts images into other formats like NDVI. Image classification automatically categorizes image pixels into land cover classes using supervised or unsupervised methods.
1) Visual image interpretation of remote sensing data can be labor intensive and require extensive training, while digital analysis techniques allow for automated and more detailed spectral analysis.
2) Preprocessing steps such as geometric correction and radiometric calibration are often needed to remove distortions in remote sensing images before analysis.
3) Image enhancement techniques such as contrast stretching, filtering, and ratioing can increase separability of features by amplifying slight spectral differences in the data.
This document discusses several approaches for atmospheric correction of remote sensing imagery:
1) Image-based methods like the dark pixel method and regression method estimate and remove atmospheric path radiance.
2) The empirical line method uses ground targets of known reflectance to model atmospheric effects.
3) Radiative transfer models precisely account for atmospheric conditions using numerical models like MODTRAN or 6S to convert pixel values to surface reflectance.
4) Relative correction methods normalize images without absolute calibration to surface reflectance. Atmospheric correction is needed to accurately analyze surface properties from remote sensing data and compare images acquired at different times or wavelengths.
Photogrammetry is the science of obtaining information about physical objects through photographs, without needing direct contact. It involves measuring and analyzing captured images. The name comes from Greek roots meaning "light", "drawing", and "to measure". Key developments included using photography for mapmaking in the 1840s-1850s, the photogrammetric stereoplotter in the 1890s, and aerial photography from balloons and planes in the 1860s-1900s, advancing the field into the digital era.
Urban Landuse/ Landcover change analysis using Remote Sensing and GISHarshvardhan Vashistha
This document provides an overview of land cover and land use change detection. It discusses techniques for detecting changes through analyzing satellite images over time. Methods include visual interpretation, image rationing, classification, and indices. Factors to consider include the objective, type of change to detect, and type of changes of interest, like land use, forest, or urban changes. Applications include monitoring land use change, deforestation, fires, wetlands, urban growth, and environmental changes. Proper selection of methods and data depends on the scale and specifics of the changes being examined.
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.
this presentation briefly describes the digital image processing and its various procedures and techniques which include image correction or rectification with remote sensing data/ images. it also contains various image classification techniques.
This document discusses methods for enhancing spatial features in images. It describes spatial filtering and convolution, which involve applying kernels or filters to images to emphasize different spatial frequencies. Low-pass filters smooth images by reducing high spatial frequencies related to fine detail, while high-pass filters have the opposite effect of sharpening images. Edge enhancement filters aim to highlight linear features by increasing contrast around edges. Fourier analysis represents images as combinations of sine and cosine waves of different frequencies and can reveal features aligned in various directions.
This document discusses remote sensing and geographical information systems in civil engineering. It covers various topics related to remote sensing sensors including optical sensors, thermal scanners, multispectral sensors, passive and active sensors, scanning and non-scanning sensors, imaging and non-imaging sensors, and the different types of resolutions including spatial, spectral, radiometric, and temporal resolution. It provides examples and illustrations of these concepts.
This document provides an overview of remote sensing basics. It defines remote sensing as acquiring information about an object without direct contact. It discusses key elements of the remote sensing process including energy sources, atmospheric interactions, data acquisition by sensors, and data analysis. It also covers topics like the electromagnetic spectrum, atmospheric scattering and absorption, atmospheric windows, and spectral signatures. The document is intended as an introduction to fundamental concepts in remote sensing.
Data models are a set of rules and/or constructs used to describe and represent aspects of the real world in a computer. GIS can handle four data models for various applications. This module explains those four.
This document discusses different types of GIS data. Spatial data represents geographic locations and features on Earth and includes data types like points, lines, and polygons. Attribute data describes characteristics of spatial features like forests stands and includes data types like tabular data. Raster data models land cover with square grid cells, while vector data represents features as points, lines, or polygons which can accurately show shape and topology. Spatial data is mapped and stored with coordinates, while attribute data describes characteristics and is often linked to spatial data in a database.
This document provides an overview of ground truthing for remote sensing. It defines ground truth as observations or measurements made near the Earth's surface to support air or space-based remote sensing. Ground truth is collected using tools like GPS, radiometers, cameras, and topographic maps. It involves field observations, spectral measurements, location coordinates and sample collection to validate remote sensing data and reduce classification errors. The process of ground truthing helps verify pixel contents on satellite images and assess classification accuracy.
Image quality assessment and statistical evaluationDocumentStory
This document discusses image quality assessment and statistical evaluation of remote sensing data. It explains that remote sensing data can contain errors introduced by the environment, sensor issues, or processing errors. Assessing image quality and statistical characteristics is important and can be done by examining histograms, pixel values, and univariate and multivariate statistics to identify anomalies or redundancy in the data. The document also provides mathematical notation for describing remote sensing data and discusses concepts like populations, samples, sampling error, and the importance of metadata for image analysis.
This document discusses different types of map projections used to represent the spherical earth on a flat surface. It describes how all projections involve some distortion of properties like shapes, areas, distances or directions. The key types are conformal, equivalent, and equidistant projections. It explains the concepts of projection surfaces like cones, cylinders and planes, as well as variables like the light source and orientation. Specific common projections are also outlined, such as Mercator, Lambert conformal conic, and azimuthal equidistant, along with their characteristic distortions and uses.
10-Image rectification and restoration.pptAJAYMALIK97
This document discusses digital image processing. It defines digital image processing as the computer-based manipulation and interpretation of digital images. It outlines seven broad types of computer-assisted operations, including image rectification and restoration, image enhancement, image classification, data merging and GIS integration, hyperspectral image analysis, biophysical modeling, and image transmission and compression. It provides details on image rectification and restoration, which involves preprocessing to correct distorted or degraded image data through techniques like geometric distortions, radiometric calibration, and noise elimination.
Types of Platforms
1. Airbrone Platforms
2. Spacebrone Platforms
Platforms are Vital Role in remote sensing data acquisition
Necessary to correct the position the remote sensors that collect data from the objects of interest
Geo synchronous and Sun synchronous SatellitesTilok Chetri
There are three main types of satellite orbits:
1) Polar orbits have an inclination of 90 degrees, allowing satellites to observe the entire Earth as it rotates. They complete an orbit every 90 minutes.
2) Sun synchronous orbits allow satellites to pass over the same location at the same local time each day. These orbits are between 700-800 km in altitude.
3) Geosynchronous orbits circle the Earth at the same rate it rotates, allowing satellites to continuously observe nearly half of the Earth. These orbits are used for weather monitoring and communication satellites. Each orbit type has advantages and disadvantages for different applications.
Galileo used early optical enhancements in the 1600s to observe celestial bodies and merchant ships arriving in harbor. In the 1880s, Arthur Batut affixed cameras to kites, including an altimeter to determine the scale of images, making him the father of kite aerial photography. By 1903, camera miniaturization allowed cameras to be attached to pigeons for aerial photography, though images were limited. During the Cuban Missile Crisis in 1962, U-2 spy planes detected Soviet nuclear missiles in Cuba using remote sensing, changing the course of history.
IMAGE INTERPRETATION TECHNIQUES of surveyKaran Patel
Image interpretation is the process of examining an aerial photo or digital remote sensing image and manually identifying the features in that image. This method can be highly reliable and a wide variety of features can be identified, such as riparian vegetation type and condition, and anthropogenic features
This document discusses three methods for measuring height from aerial photographs: relief displacement, shadow length, and stereoscopic parallax. Relief displacement measures height by how far an object is shifted from its true position in an aerial photo due to its elevation. Shadow length measures height by using the length of an object's shadow and the sun angle. Stereoscopic parallax measures height by comparing the difference in an object's position between two overlapping aerial photos taken from different positions. Formulas are provided for calculating height from measurements obtained using each of these three methods.
This document provides an overview of digital image processing techniques. It defines digital image processing as the manipulation of digital image data using computer hardware and software. The document outlines several key stages in digital image processing, including pre-processing, image enhancement, image filtering, image transformation, and image classification. Pre-processing involves correcting geometric distortions and radiometric errors in remote sensing data. Image enhancement improves image quality. Image filtering separates images into spatial frequencies. Image transformation converts images into other formats like NDVI. Image classification automatically categorizes image pixels into land cover classes using supervised or unsupervised methods.
1) Visual image interpretation of remote sensing data can be labor intensive and require extensive training, while digital analysis techniques allow for automated and more detailed spectral analysis.
2) Preprocessing steps such as geometric correction and radiometric calibration are often needed to remove distortions in remote sensing images before analysis.
3) Image enhancement techniques such as contrast stretching, filtering, and ratioing can increase separability of features by amplifying slight spectral differences in the data.
This document discusses digital image processing of satellite images. It describes how satellite images are represented digitally as pixels with brightness values. It explains that digital image processing involves three main categories: image rectification and restoration to correct geometric distortions; enhancement to improve visual interpretation; and information extraction to automate feature identification. It provides details on image rectification, color composites, and basic image enhancement techniques for satellite images.
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.
This document describes a system for visualizing deviations on arbitrary surfaces for quality assurance and process control. The system combines 3D measurement devices like terrestrial laser scanners with a projector. This allows detected deviations between a reference model (like CAD data) and measured data to be projected directly onto the measured object. The key steps are determining the relative orientation of the measurement device and projector, computing an inspection map of deviations, and projecting this map. A novel approach called "virtual targets" is presented for establishing the relative orientation without using physical targets when laser scanning. Sample applications for quality inspection and object alignment are demonstrated.
Remote Sensing Sattelite image Digital Image Analysis.pptxhabtamuawulachew1
This document provides an outline and overview of digital image analysis. It defines digital image analysis as the manipulation of digital images with a computer. It discusses several key techniques including image pre-processing (radiometric correction, geometric correction, subsetting, layer stacking, and mosaicking), image enhancement (contrast manipulation and spatial feature manipulation), and multi-image manipulation. The document also lists two lab exercises - performing band rationing on a Landsat 8 image of Lake Tana basin and generating an NDVI from the image.
Effect of sub classes on the accuracy of the classified imageiaemedu
This document discusses image classification techniques in remote sensing. It begins with an overview of the need for geometric corrections and rectification of satellite images to account for distortions. It then describes supervised and unsupervised classification methods for extracting land cover information from images. Supervised classification involves using training data to classify pixels, while unsupervised classification groups pixels into spectral classes based on natural clusters. The maximum likelihood algorithm assumes normal distributions and assigns pixels to the most probable class. Classification accuracy is assessed using an error matrix to evaluate omission and commission errors between the classified and reference maps. Increasing the number of classes in a classified image can reduce accuracy by making spectral distinctions between classes less clear.
This document provides an overview of the application of remote sensing and geographical information systems in civil engineering. It discusses key concepts such as image interpretation, data preprocessing, feature extraction, image classification, and accuracy assessment. The document aims to explain how remote sensing and GIS techniques can be used to extract useful information from imagery and geospatial data for civil engineering applications.
Digital images can be manipulated mathematically by treating pixel brightness values as numbers. This document discusses various digital image processing techniques including:
1. Image rectification to correct geometric distortions and calibrate radiometric data. This involves techniques like geometric corrections to adjust for sensor distortions and radiometric corrections to standardize brightness values.
2. Image enhancement techniques like contrast stretching to increase image contrast and improve feature detection. Methods include linear stretches, histogram equalization, and logarithmic transforms.
3. Local operations called spatial filtering that modify pixel values based on neighboring pixels. This can emphasize or de-emphasize certain image features or spatial frequencies to enhance details or reduce noise.
The document analyzes geometric distortions in imagery from the HJ-1A/B satellites and compares methods for geometric correction. It finds:
1) HJ-1A/B CCD imagery has both global and local geometric distortions even after initial correction.
2) Polynomial, thin plate splines, and finite element models were tested for correction using control points. Polynomial modeling performed worst while finite element modeling produced the best results with enough evenly distributed points.
3) Finite element modeling is recommended for precise geometric correction of HJ-1A/B imagery as it is a local method that provides stability and accuracy, especially with over 1,000 control points.
A Flexible Scheme for Transmission Line Fault Identification Using Image Proc...IJEEE
This paper describes a methodology that aims to find and diagnosing faults in transmission lines exploitation image process technique. The image processing techniques have been widely used to solve problem in process of all areas. In this paper, the methodology conjointly uses a digital image process Wavelet Shrinkage function to fault identification and diagnosis. In other words, the purpose is to extract the faulty image from the source with the separation and the co-ordinates of the transmission lines. The segmentation objective is the image division its set of parts and objects, which distinguishes it among others in the scene, are the key to have an improved result in identification of faults.The experimental results indicate that the proposed method provides promising results and is advantageous both in terms of PSNR and in visual quality.
REGION CLASSIFICATION AND CHANGE DETECTION USING LANSAT-8 IMAGESADEIJ Journal
The change detection in remote sensing images remains an important and open problem for damage assessment. A new change detection method for LANSAT-8 images based on homogeneous pixel transformation (HPT) is proposed. Homogeneous Pixel Transformation transfers one image from its original feature space (e.g., gray space) to another feature space (e.g., spectral space) in pixel-level to make the pre-event images and post-event images to be represented in a common space or projection space for the convenience of change detection. HPT consists of two operations, i.e., forward transformation and backward transformation. In the forward transformation, each pixel of pre-event image in the first feature space is taken and will estimate its mapping pixel in the second space corresponding to post-event image based on the known unchanged pixels. A multi-value estimation method with the noise tolerance is produced to determine the mapping pixel using K-nearest neighbours technique. Once the mapping pixels of pre-event image are identified, the difference values between the mapping image and the post-event image can be directly generated. Then the similar work is done for backward transformation to combine the post-event image with the first space, and one more difference value for each pixel will be generated. Then, the two difference values are taken and combined to improve the robustness of detection with respect to the noise and heterogeneousness of images. (FRFCM) Fast and Robust Fuzzy C-means clustering algorithm is employed to divide the integrated difference values into two clusters- changed pixels and unchanged pixels. This detection results may contain few noisy regions as small error detections, and a spatial-neighbor based noise filter is developed to reduce the false alarms and missing detections. The experiments for change detection with real images of LANSAT-8 in Tuticorin between 2013-2019 are given to validate the percentage of the changed regions in the proposed method.
Development of stereo matching algorithm based on sum of absolute RGB color d...IJECEIAES
This article presents local-based stereo matching algorithm which comprises a devel- opment of an algorithm using block matching and two edge preserving filters in the framework. Fundamentally, the matching process consists of several stages which will produce the disparity or depth map. The problem and most challenging work for matching process is to get an accurate corresponding point between two images. Hence, this article proposes an algorithm for stereo matching using improved Sum of Absolute RGB Differences (SAD), gradient matching and edge preserving filters. It is Bilateral Filter (BF) to surge up the accuracy. The SAD and gradient matching will be implemented at the first stage to get the preliminary corresponding result, then the BF works as an edge-preserving filter to remove the noise from the first stage. The second BF is used at the last stage to improve final disparity map and increase the object boundaries. The experimental analysis and validation are using the Middlebury standard benchmarking evaluation system. Based on the results, the proposed work is capable to increase the accuracy and to preserve the object edges. To make the proposed work more reliable with current available methods, the quantitative measurement has been made to compare with other existing methods and it shows the proposed work in this article perform much better.
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.
V. Karthikeyan proposes a novel histogram-based image registration technique. The method segments images using multiple histogram thresholds to extract objects. Extracted objects are characterized by attributes like area, axis ratio, and fractal dimension. Objects between images are matched to estimate rotation and translation. The technique was tested on pairs of images with different rotations and translations and achieved sub-pixel accuracy in registration. The method outperformed other techniques like SIFT for remote sensing images. Future work could optimize the segmentation and apply the technique to multispectral images.
Improving image resolution through the cra algorithm involved recycling proce...csandit
Image processing concepts are widely used in medical fields. Digital images are prone to a
variety of types of noise. Noise is the result of errors in the image acquisition process for
reconstruction that result in pixel values that reflect the true intensities of the real scenes. A lot
of researchers are working on the field analysis and processing of multi-dimensional images.
Work previously hasn’t sufficient to stop them, so they continue performance work is due by the
researcher. In this paper we contribute a novel research work for analysis and performance
improvement about to image resolution. We proposed Concede Reconstruction Algorithm (CRA)
Involved Recycling Process to reduce the remained problem in improvement part of an image
processing. The CRA algorithms have better response from researcher to use them
IMPROVING IMAGE RESOLUTION THROUGH THE CRA ALGORITHM INVOLVED RECYCLING PROCE...cscpconf
Image processing concepts are widely used in medical fields. Digital images are prone to a variety of types of noise. Noise is the result of errors in the image acquisition process for
reconstruction that result in pixel values that reflect the true intensities of the real scenes. A lot of researchers are working on the field analysis and processing of multi-dimensional images. Work previously hasn’t sufficient to stop them, so they continue performance work is due by the researcher. In this paper we contribute a novel research work for analysis and performance improvement about to image resolution. We proposed Concede Reconstruction Algorithm (CRA)
Involved Recycling Process to reduce the remained problem in improvement part of an image processing. The CRA algorithms have better response from researcher to use them.
Matching algorithm performance analysis for autocalibration method of stereo ...TELKOMNIKA JOURNAL
Stereo vision is one of the interesting research topics in the computer vision field. Two cameras are used to generate a disparity map, resulting in the depth estimation. Camera calibration is the most important step in stereo vision. The calibration step is used to generate an intrinsic parameter of each camera to get a better disparity map. In general, the calibration process is done manually by using a chessboard pattern, but this process is an exhausting task. Self-calibration is an important ability required to overcome this problem. Self-calibration required a robust and good matching algorithm to find the key feature between images as reference. The purpose of this paper is to analyze the performance of three matching algorithms for the autocalibration process. The matching algorithms used in this research are SIFT, SURF, and ORB. The result shows that SIFT performs better than other methods.
The cost of acquiring information by natural selectionCarl Bergstrom
This is a short talk that I gave at the Banff International Research Station workshop on Modeling and Theory in Population Biology. The idea is to try to understand how the burden of natural selection relates to the amount of information that selection puts into the genome.
It's based on the first part of this research paper:
The cost of information acquisition by natural selection
Ryan Seamus McGee, Olivia Kosterlitz, Artem Kaznatcheev, Benjamin Kerr, Carl T. Bergstrom
bioRxiv 2022.07.02.498577; doi: https://doi.org/10.1101/2022.07.02.498577
Current Ms word generated power point presentation covers major details about the micronuclei test. It's significance and assays to conduct it. It is used to detect the micronuclei formation inside the cells of nearly every multicellular organism. It's formation takes place during chromosomal sepration at metaphase.
The technology uses reclaimed CO₂ as the dyeing medium in a closed loop process. When pressurized, CO₂ becomes supercritical (SC-CO₂). In this state CO₂ has a very high solvent power, allowing the dye to dissolve easily.
When I was asked to give a companion lecture in support of ‘The Philosophy of Science’ (https://shorturl.at/4pUXz) I decided not to walk through the detail of the many methodologies in order of use. Instead, I chose to employ a long standing, and ongoing, scientific development as an exemplar. And so, I chose the ever evolving story of Thermodynamics as a scientific investigation at its best.
Conducted over a period of >200 years, Thermodynamics R&D, and application, benefitted from the highest levels of professionalism, collaboration, and technical thoroughness. New layers of application, methodology, and practice were made possible by the progressive advance of technology. In turn, this has seen measurement and modelling accuracy continually improved at a micro and macro level.
Perhaps most importantly, Thermodynamics rapidly became a primary tool in the advance of applied science/engineering/technology, spanning micro-tech, to aerospace and cosmology. I can think of no better a story to illustrate the breadth of scientific methodologies and applications at their best.
Immersive Learning That Works: Research Grounding and Paths ForwardLeonel Morgado
We will metaverse into the essence of immersive learning, into its three dimensions and conceptual models. This approach encompasses elements from teaching methodologies to social involvement, through organizational concerns and technologies. Challenging the perception of learning as knowledge transfer, we introduce a 'Uses, Practices & Strategies' model operationalized by the 'Immersive Learning Brain' and ‘Immersion Cube’ frameworks. This approach offers a comprehensive guide through the intricacies of immersive educational experiences and spotlighting research frontiers, along the immersion dimensions of system, narrative, and agency. Our discourse extends to stakeholders beyond the academic sphere, addressing the interests of technologists, instructional designers, and policymakers. We span various contexts, from formal education to organizational transformation to the new horizon of an AI-pervasive society. This keynote aims to unite the iLRN community in a collaborative journey towards a future where immersive learning research and practice coalesce, paving the way for innovative educational research and practice landscapes.
EWOCS-I: The catalog of X-ray sources in Westerlund 1 from the Extended Weste...Sérgio Sacani
Context. With a mass exceeding several 104 M⊙ and a rich and dense population of massive stars, supermassive young star clusters
represent the most massive star-forming environment that is dominated by the feedback from massive stars and gravitational interactions
among stars.
Aims. In this paper we present the Extended Westerlund 1 and 2 Open Clusters Survey (EWOCS) project, which aims to investigate
the influence of the starburst environment on the formation of stars and planets, and on the evolution of both low and high mass stars.
The primary targets of this project are Westerlund 1 and 2, the closest supermassive star clusters to the Sun.
Methods. The project is based primarily on recent observations conducted with the Chandra and JWST observatories. Specifically,
the Chandra survey of Westerlund 1 consists of 36 new ACIS-I observations, nearly co-pointed, for a total exposure time of 1 Msec.
Additionally, we included 8 archival Chandra/ACIS-S observations. This paper presents the resulting catalog of X-ray sources within
and around Westerlund 1. Sources were detected by combining various existing methods, and photon extraction and source validation
were carried out using the ACIS-Extract software.
Results. The EWOCS X-ray catalog comprises 5963 validated sources out of the 9420 initially provided to ACIS-Extract, reaching a
photon flux threshold of approximately 2 × 10−8 photons cm−2
s
−1
. The X-ray sources exhibit a highly concentrated spatial distribution,
with 1075 sources located within the central 1 arcmin. We have successfully detected X-ray emissions from 126 out of the 166 known
massive stars of the cluster, and we have collected over 71 000 photons from the magnetar CXO J164710.20-455217.
ESPP presentation to EU Waste Water Network, 4th June 2024 “EU policies driving nutrient removal and recycling
and the revised UWWTD (Urban Waste Water Treatment Directive)”
ESA/ACT Science Coffee: Diego Blas - Gravitational wave detection with orbita...Advanced-Concepts-Team
Presentation in the Science Coffee of the Advanced Concepts Team of the European Space Agency on the 07.06.2024.
Speaker: Diego Blas (IFAE/ICREA)
Title: Gravitational wave detection with orbital motion of Moon and artificial
Abstract:
In this talk I will describe some recent ideas to find gravitational waves from supermassive black holes or of primordial origin by studying their secular effect on the orbital motion of the Moon or satellites that are laser ranged.
2. Introduction
2 Visual image interpretation techniques have certain disadvantages and may
require extensive training and are labor intensive.
In this technique, the spectral characteristics are not always fully evaluated
because of the limited ability of the eye to discern tonal value and analyze
the spectral changes.
If the data are in digital mode, the remote sensing data can be analyzed
using digital image processing techniques and such a data base can be used
in Raster GIS.
In applications where spectral patterns are more informative, it is
preferable to analyze digital data rather than pictorial data.
3. Definition
Digital Image Processing is the manipulation of the digital data with
the help of the computer hardware and software to produce digital
maps in which specific information has been extracted and
highlighted.
3
4. 4
The Origin of Digital image Processing
. The first application was in newspaper industry in 1920s
5. Different Stages in Digital Image Processing
Satellite remote sensing data in general and digital data in particular have
been used as basic inputs for the inventory and mapping of natural
resources of the earth surface like forestry, soils, geology and agriculture.
Space borne remote sensing data suffer from a variety of radiometric,
atmospheric and geometric errors, earth‟s rotation and so on.
These distortions would diminish the accuracy of the information
extracted and reduce the utility of the data. So these errors required to
be corrected.
5
6. In order to update and compile maps with high accuracy, the satellite digital
data have to be manipulated using image process techniques.
Digital image data is fed into the computer, and the computer is
programmed to insert these data into an equation or a series of equation,
and then store the results of the competition of each and every pixel.
These results are called look–up–table (LTU) values for a new image that
may be manipulated further to extract information of user‟s interest.
Virtually, all the procedure may be grouped into one or more of the
following broad types of the operations, namely,
1. pre – processing, and
2. Image Processing.
7. Pre – processing
Remotely sensed raw data generally contains flaws and deficiencies received
from imaging sensor mounted on the satellite. The correction of deficiencies
and removal of flaws present in the data through some methods are termed as
pre–processing methods this correction model involves to correct geometric
distortions, to calibrate the data radiometrically and to eliminate noise
present in the data. All the pre–processing methods are consider under three
heads, namely,
a) Radiometric correction method
b) Atmospheric correction method
c) Geometric correction methods
7
8. Radiometric correction method
Radiometric errors are caused by detected imbalance and
atmospheric deficiencies. Radiometric corrections are
transformation on the data in order to remove error, which
are geometrically independent. Radiometric corrections are
also called as cosmetic corrections and are done to improve
the visual appearance of the image. Some of the radiometric
distortions are as follows:
1. Correction for missing lines
2. Correction for periodic line striping
3. Random noise correction
8
13. Atmospheric correction method
The value recorded at any pixel location on the remotely sensed image
is not a record of the true ground – leaving radiant at that point, for the
signal is attenuated due to absorption and scattering.
The atmosphere has effect on the measured brightness value of a pixel.
Other difficulties are caused by the variation in the illumination
geometry.
Atmospheric path radiance introduces haze in the imagery where by
decreasing the contrast of the data.
13
16. Geometric correction methods
Remotely sensed images are not maps. Frequently information extracted
from remotely sensed images is integrated with the map data in
Geographical Information System (GIS). The transformation of the
remotely sensed image into a map with the scale and projection properties is
called geometric corrections. Geometric corrections of remotely sensed
image is required when the image is to be used in one of the following
circumstances:
I. Totransform an image to match your map projection
II. Tolocate points of the interest on map and image.
III. To overlay temporal sequence of images of the same area, perhaps acquired
by different sensors.
IV. Tointegrate remotely sensed data with GIS.
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21. Systematic Correction: when the geometric reference data or geometry
of sensor are given or measured, the geometric distortion can be
systematically avoided. Generally systematic correction is sufficient to
remove all errors.
Non-systematic Correction: polynomials to transform from a geographic
coordinate system, or vice versa, will be determined with the given
coordinates of the ground control points using the least square method.
The accuracy depends on the order of the polynomials and the number and
distribution of ground control points.
Combined method: Firstly systematic correction is applied, then the
residual errors are reduced using lower order polynomials. Usually the
goal of geometric correction is to obtain an error within plus or minus one
pixel of its true position.
21
22. must be
Internal errors are due to sensor effects, being systematic or stationary.
External errors are due to platform perturbations and scene characteristics
which are invariable in nature and can be determined from ground control
and tracing data.
In order to remove the haze component due to these disturbances to simple
techniques are applied:
I. Histogram Minimum Method, and
II. Regression Method.
To correct sensor data with GIS internal and external errors
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determined and be either predictable or measureable.
23. 23
Fig 1.10: It is an example of a contrast
stretch. Histogram A represents the pixel
values in image A. By stretching the values
(shown in histogram B) across the entire
range, you can alter and visually enhance
the appearance of the image (image B).
24. Image Processing:
The second stage in Digital Image Processing entailsfive
different operations, namely,
A.Image Registration
B.Image Enhancement
C.Image Filtering
D.Image Transformation
E. Image Classification
All these are discussed in details below:
24
25. Image Registration
Image registration is the translation and alignment process by which two images/maps of
like geometrics and of the same set of objects are positioned co-incident with respect to
one another so that corresponding element of the same ground area appear in the same
place on the registered images.
This is often called image to image registration. One more important concept with respect
to geometry of satellite image is rectification. Rectification is the process by which the
geometry of an image area is made planimetric.
This process almost always involves relating Ground Control Point (GCP), pixel
coordinates with precise geometric correction since each pixel can be referenced not
only by the row or column in a digital image, but it is also rigorously referenced in
degree, feet or meters in a standard map projection whenever accurate data, direction and
distance measurements are acquired, geometric rectification is required.
This is often called as image to map rectification.
28. Image Enhancement
Low sensitivity of the detector, weak signal of the objects present on the
earth surface, similar reflectance of different objects and environmental
conditions at the time of recording are the major causes of low contrast of
the image. Another problem that complicates photographic display of
digital image is that the human eye is poor at discriminating the slight
spectral or radiometric differences that may characterize the feature.
Image enhancement techniques improve the quality of an image as
perceived by human eye. There exist a wide variety of techniques for
edge
used
improving image quality. contrast stretch, density slicing,
enhancement, and spatial filtering are the most commonly
techniques.
Image enhancement method are applied separately to each band of a
multispectral image.
28
33. Image Filtering
34
A characteristic of remotely sensed images is a parameter called spatial
frequency, defined as number of Changes in brighter value per unit distance for
any particular part of an image. Few brighter changes in an image considered as
low frequency, conversely, the brighter value changes dramatically over short
distance, this is an area of high frequency.
Spatial filtering is the process of dividing the image into its constituent spatial
frequency and selectively altering certain spatial features. This technique
increases the analyst‟s ability to discriminate details. The three types of spatial
filters used in remote sensor data processing are:
1. Low Pass Filters,
2. Band Pass Filters, and
3. High Pass Filters.
36. 37
Figure 1.17: In the example, we add some sinusoidal noise to an
image and filter it with Band Pass Filter
37. Image Transformation
All the transformations in an image based on arithmetic
operations. The resulting images may will have properties
that make them more suited for a particular purpose than the
original.
The transformation which is commonly used as follows:
Principal Component Analysis (PCA)
38
38. Principal Component Analysis (PCA)
The application of Principal Component Analysis (PCA) on raw remotely
sensed data producing a new image which is more interpretable than the
original data. The main advantage of PCA is that it may be used to compress
the information content of a number of bands into just two or three
transformed principal component images.
42
39. 43
Figure 1.20: The application of Principal
Component Analysis (PCA) on raw remotely
sensed data of UrbanArea by Unsupervised
Classification.
40. Image Classification
44
Image Classification is a procedure to automatically categorise all pixel in an
image of a terrain into land use and land cover classes. This concept is dealt
under broad subject, namely, “pattern recognition”. Spectral pattern recognition
refers to the family of classification procedures that utilises this pixel-by-pixel
spectral information as the basis for automated land cover classification. Spatial
pattern recognition of image on the basis of the spatial relationship with pixel
surrounding.
Image classification technique are grouped into two types, namely,
1. Supervised Classification , and
2. Unsupervised Classification.
41. Supervised Classification
Different phases in supervised Classification technique are as
follows:
(i) Appropriate classification scheme for analysis is adopted.
(ii)Selection of representative training sites and collection of
spectral signatures.
(iii)Evaluation of statics for the training site spectral data.
(iv)The statics are analyzed to select the appropriate features to
be used in classification process.
(v)Appropriate classification algorithm is selected.
(vi)Then classify the image into „n‟number ofclasses.
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43. Unsupervised Classification
47
The Unsupervised Classifiers do not utilize training data as basis for classification. These classifiers examine the
unknown pixel and aggregate them into a number of class based on natural grouping or clusters present in the image
values. The classes that result from unsupervised classification are spectral classes.
The analyst must compare the classified data into some form of reference data to identify the informational value of the
spectral classes.
It has been found that in areas of complex terrain, the unsupervised approach is preferable. In such conditions, if the
supervised approach is used, the user will have difficulty in selecting training sites because of variability of spectral
response within them each class.
Consequently, group data collected but it is very time consuming. Also, the supervised approach is subjective in the sense
that the analyst tries to classify information categories, which are often composed of several spectral classes, whereas
spectrally distinguishable classes will be revealed by the unsupervised approach.
Additionally, the unsupervised approach has potential advantage of revealing discrimination classes unknown from the
previous supervised classification.
44. 48
Figure 1.22: In an unsupervised classification, a thematic map of the input image is created
automatically in ENVI without requiring any pre-existing knowledge of the input image. The
unsupervised classification method performs an automatic grouping; the next step in this
workflow is for the user to assign names to each of the automatically generated regions (for
45. References
Campbell, J.B. and Wynne, R. H. (2010). Introduction to
Remote Sensing, The Guilford Press, New York.pp.
Jayaraman, S., Esakkirajan, S. and Veerakumar, T.(2016).
Digital Image Processing, McGraw Hill Education (India)
pvt. Ltd., New Delhi. pp. 243-297
Bhatta, B.(2015). Remote Sensing and GIS, Oxford
university press, New Delhi. pp. 196–223
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