Sensor Network for Landslide Monitoring With Laser Ranging System Avoiding Rainfall Influence on Laser Ranging by Means of Time Diversity and Satellite Imagery Data Based Landslide Disaster Relief
Sensor network for landslide monitoring with laser ranging system is developed together with landslide disaster relief with remote sensing satellite imagery data. Time diversity is utilized for rainfall influence avoidance in the distance measurements between laser ranging equipment and targets. Also automatic tie point extraction method is proposed. Experimental results show that (1) the proposed time diversity of the laser ranging measurement does work for avoidance from rainfall influence; (2) the proposed automatic control point extraction method does work for tie point matching together with change detection for landslide disaster relief.
This document discusses various techniques for monitoring landslides, including remote sensing, photogrammetry, ground-based surveying, GPS, and geotechnical methods. Remote sensing techniques discussed include synthetic aperture radar (SAR), interferometric SAR (InSAR), and RADAR systems which use radio waves to detect ground movement. Photogrammetry allows interpretation of aerial photos to identify landslides. Ground surveying employs techniques like triangulation and leveling. GPS provides location and velocity data through satellite signals. Geotechnical sensors monitor deformation underground through extensometers, inclinometers, piezometers, and other instruments.
PERSISTENT SCATTERER SAR INTERFEROMETRY APPLICATION.pptxgrssieee
This document discusses the application of persistent scatterer interferometry (PSI) to study landslides in the Berkeley Hills. PSI uses phase information from SAR images taken at different times to measure surface deformation with millimeter accuracy. It was applied using Envisat, ERS, Radarsat, and TerraSAR-X data. Thousands of persistent scatterers were identified, allowing measurement of surface motion along the Hayward Fault and within landslides. Future work will continue monitoring with additional SAR data to better resolve three-dimensional landslide motions.
The document discusses the use of Synthetic Aperture Radar (SAR) and InSAR techniques for monitoring solid earth geophysics hazards. SAR uses microwaves to generate high-resolution images of the Earth's surface independently of solar illumination. InSAR uses multiple SAR images to measure surface changes down to the centimeter scale, such as caused by earthquakes or subsidence. It discusses various InSAR techniques including DifSAR, Persistent Scatterer InSAR, and Corner Reflector InSAR and their applications in oil and gas, mining, infrastructure and hazard monitoring. The document also lists several commercial and open-source InSAR processing software packages.
This presentation cover description of microwave remote sensing, Active and Passive Microwave remote sensing, RADAR, Slant range distortion like Foreshortening and Layover, Sar image and some Recent works in where microwave remote sensing has used to detect natural calamities
This document summarizes a seminar on remote sensing technologies guided by Mr. B. S. Manjare from the P.G. Department of Geology at Rashtrasant Tukadoji Maharaj Nagpur University. The seminar covered the history and objectives of India's space program, types of satellites and launch vehicles used, characteristics of the Indian Remote Sensing satellite series, applications of remote sensing in geology, and India's roadmap for future space missions. Key topics discussed included the various generations of IRS satellites with improving sensors, applications of remote sensing for natural resource management and disaster monitoring, and India's goals of developing reusable launch vehicles and satellites with advanced capabilities.
This document discusses how Interferometric Synthetic Aperture Radar (InSAR) works to measure ground deformation. It explains that InSAR uses the phase difference between two SAR images of the same area taken at different times to detect millimeter-scale changes in the distance to ground targets. It provides examples of how InSAR has been used to measure subsidence from earthquakes and other natural hazards. The document also notes some limitations of InSAR related to decorrelation from changes on the ground surface and in the atmosphere between image acquisitions.
This document provides an overview of remote sensing concepts. It defines remote sensing as acquiring information about an object without physical contact. Remote sensing data is collected from platforms like satellites and aircraft and analyzed. The document outlines the electromagnetic spectrum, how energy interacts with the atmosphere and objects, different sensor and image types, and resolutions. It also defines key terms like digital image, satellite imagery, spectral signature, and discusses different platform and sensor types used in remote sensing.
This document discusses various techniques for monitoring landslides, including remote sensing, photogrammetry, ground-based surveying, GPS, and geotechnical methods. Remote sensing techniques discussed include synthetic aperture radar (SAR), interferometric SAR (InSAR), and RADAR systems which use radio waves to detect ground movement. Photogrammetry allows interpretation of aerial photos to identify landslides. Ground surveying employs techniques like triangulation and leveling. GPS provides location and velocity data through satellite signals. Geotechnical sensors monitor deformation underground through extensometers, inclinometers, piezometers, and other instruments.
PERSISTENT SCATTERER SAR INTERFEROMETRY APPLICATION.pptxgrssieee
This document discusses the application of persistent scatterer interferometry (PSI) to study landslides in the Berkeley Hills. PSI uses phase information from SAR images taken at different times to measure surface deformation with millimeter accuracy. It was applied using Envisat, ERS, Radarsat, and TerraSAR-X data. Thousands of persistent scatterers were identified, allowing measurement of surface motion along the Hayward Fault and within landslides. Future work will continue monitoring with additional SAR data to better resolve three-dimensional landslide motions.
The document discusses the use of Synthetic Aperture Radar (SAR) and InSAR techniques for monitoring solid earth geophysics hazards. SAR uses microwaves to generate high-resolution images of the Earth's surface independently of solar illumination. InSAR uses multiple SAR images to measure surface changes down to the centimeter scale, such as caused by earthquakes or subsidence. It discusses various InSAR techniques including DifSAR, Persistent Scatterer InSAR, and Corner Reflector InSAR and their applications in oil and gas, mining, infrastructure and hazard monitoring. The document also lists several commercial and open-source InSAR processing software packages.
This presentation cover description of microwave remote sensing, Active and Passive Microwave remote sensing, RADAR, Slant range distortion like Foreshortening and Layover, Sar image and some Recent works in where microwave remote sensing has used to detect natural calamities
This document summarizes a seminar on remote sensing technologies guided by Mr. B. S. Manjare from the P.G. Department of Geology at Rashtrasant Tukadoji Maharaj Nagpur University. The seminar covered the history and objectives of India's space program, types of satellites and launch vehicles used, characteristics of the Indian Remote Sensing satellite series, applications of remote sensing in geology, and India's roadmap for future space missions. Key topics discussed included the various generations of IRS satellites with improving sensors, applications of remote sensing for natural resource management and disaster monitoring, and India's goals of developing reusable launch vehicles and satellites with advanced capabilities.
This document discusses how Interferometric Synthetic Aperture Radar (InSAR) works to measure ground deformation. It explains that InSAR uses the phase difference between two SAR images of the same area taken at different times to detect millimeter-scale changes in the distance to ground targets. It provides examples of how InSAR has been used to measure subsidence from earthquakes and other natural hazards. The document also notes some limitations of InSAR related to decorrelation from changes on the ground surface and in the atmosphere between image acquisitions.
This document provides an overview of remote sensing concepts. It defines remote sensing as acquiring information about an object without physical contact. Remote sensing data is collected from platforms like satellites and aircraft and analyzed. The document outlines the electromagnetic spectrum, how energy interacts with the atmosphere and objects, different sensor and image types, and resolutions. It also defines key terms like digital image, satellite imagery, spectral signature, and discusses different platform and sensor types used in remote sensing.
This document provides an overview of remote sensing through a seminar presented by Ashwathy Babu Paul. It defines remote sensing as obtaining information about an object without physical contact through electromagnetic radiation. It describes the basic components and process of remote sensing systems including energy sources, sensor recording, transmission and processing. Various sensors and platforms are discussed along with advantages and applications in fields like agriculture, natural resource management, national security, geology, meteorology, and more. Challenges are addressed but advantages of remote sensing are said to far outweigh these.
Hyperspectral remote sensing for oil explorationJayanth Joshua
Hyperspectral remote sensing uses sensors that collect data across a wide range of electromagnetic wavelengths, with more than 100 contiguous bands that provide detailed spectral signatures. This allows identification of subtle mineral and material differences that can indicate oil and gas deposits. Seeps at the surface cause alterations detectable by hyperspectral analysis, like calcite, pyrite and clay changes. A Hydrocarbon Index highlights absorption peaks related to hydrocarbons. Classification algorithms like Spectral Angle Mapper can map hydrocarbon-bearing zones by comparing spectra to known samples. Soil tonal anomalies from bleaching or iron/clay changes also indicate subsurface structures and seepage areas for exploration.
Indian remote sensing satellite,potentials with a revolutionSamikshya Kar
Indian Remote Sensing satellites provide remote sensing services to India. Data from these satellites are used for various applications like crop production estimation, drought monitoring, land use mapping, and more. The Cartosat-1 satellite has panchromatic cameras with a spatial resolution of 2.5 meters. It collects data that is processed and analyzed to derive information about vegetation, water bodies, and other landscape features with various applications such as malaria risk mapping. Remote sensing technologies allow analyzing large regions from space and measuring energy beyond human vision to enable global monitoring.
Applications of remote sensing in geological aspectsPramoda Raj
Remote sensing uses sensors on airborne or spaceborne platforms to detect and record electromagnetic radiation from the Earth's surface. It has two main phases - data acquisition through sensors and data analysis. In geology, remote sensing is used to map lithology, structural features, and monitor hazards. It helps identify rock types and structures that can indicate mineral or oil and gas deposits. Remote sensing provides synoptic data to study geomorphology, hydrology, and other Earth processes over large areas.
This document discusses different types of remote sensing systems used in civil engineering, including optical, photogrammetric, thermal, multispectral, hyperspectral, and panchromatic systems. It provides examples and specifications of various sensors, such as MODIS, AVIRIS, IKONOS, and WorldView. The document also covers digital image formats, photogrammetry, image distortions and displacements, reference ellipsoids, relief displacement, and methods of measuring heights from aerial photographs.
This document provides information on various remote sensing platforms and Earth observing satellites. It discusses balloons, helicopters, airplanes and satellites as remote sensing platforms. It then describes different types of satellite orbits and provides details on several major Earth observing satellites including their sensors and specifications. These satellites include Landsat, SPOT, Ikonos, AVHRR, Radarsat, GOES, Meteosat, and some Indian, Japanese, European and Russian satellites.
Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+,
and EO-1 ALI sensors
Gyanesh Chander a,⁎, Brian L. Markham b, Dennis L. Helder c
a SGT, Inc. 1 contractor to the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center, Sioux Falls, SD 57198-0001, USA
b National Aeronautics and Space Administration (NASA) Goddard Space Flight Center (GSFC), Greenbelt, MD 20771, USA
c South Dakota State University (SDSU), Brookings, SD 57007, USA
Application of Remote Sensing in Civil EngineeringIEI GSC
Presentation cum talk delivered by Dr Anjana Vyas, Dean CEPT University, Ahmedabad during 31st National Convention of Civil Engineering organized by The Institution of Engineers (India) Gujarat State Center, Ahmedabad
Geographic information system and remote sensingDhiren Patel
This document provides an overview of remote sensing and geographic information systems. It discusses the history of remote sensing from early aerial photography to modern satellite systems. Both passive and active remote sensing techniques are described, along with common applications in fields like forestry, agriculture, and land use analysis. Optical, radar, and lidar remote sensing systems are outlined. The document also introduces concepts in photogrammetry, surveying, and geographic information systems, including data structures and components of GIS.
Chronological Calibration Methods for Landsat Satellite Images iosrjce
This document describes methods for chronologically calibrating Landsat satellite images to account for differences when images are taken days apart. It discusses correcting ETM+ images for scan line failures and converting digital numbers to reflectance. Two methods are proposed to remove phenological effects between Landsat 7 and 8 images taken 8 days apart: linear regression and cross-correlation. Image classification using the visible red and near infrared bands is used to validate the correction methods by comparing land cover detection in study area images.
The document discusses remote sensing satellites. It begins by defining remote sensing as obtaining information about an object through analysis of data acquired from a distance without physical contact. There are two broad categories of remote sensing based on platforms: aerial and satellite. Satellite remote sensing has advantages like continuous data acquisition and broad area coverage. Remote sensing systems are classified based on the radiation source as passive or active, and based on spectral regions as optical, thermal infrared, or microwave. Key resolutions for remote sensing include spatial, spectral, temporal, and radiometric. Common applications are land cover mapping, change detection, flood monitoring, and more. Major satellite missions discussed are Landsat, SPOT, and IKONOS.
Remote Sensing For Environmental Impact And MonitoringPaulDavidShaw
This document summarizes several remote sensing technologies used for environmental monitoring, including video, LIDAR, hyperspectral sensing, and INSAR. Video can be integrated with GPS to monitor environments. LIDAR uses light to map underwater environments up to 60m deep or 30 sq km areas on land with accuracy of 3m horizontally and 0.15m vertically. Hyperspectral sensors can identify vegetation and minerals by their light absorption characteristics across 288 wavelengths. INSAR uses phase differences from satellites to measure ground displacement with millimeter accuracy, useful for monitoring earthquakes, subsidence, and dam movement.
This document discusses data collection methods for spatial and non-spatial data. It describes different types of data like raster, vector, and attribute data. Methods of data collection include land surveying techniques like chain surveying and using total stations, as well as remote sensing using aerial photography and satellite imagery. Common data sources are provided by organizations like the Survey of India and ISRO. The document also covers topics like digitization in GIS and using software like AutoCAD.
Remote sensing uses sensors on satellites or aircraft to detect and record electromagnetic radiation from the Earth's surface in order to gather information about it without direct contact. There are six main elements of remote sensing: an energy source, radiation and the atmosphere, interaction with the target, recording by the sensor, transmission and processing, and interpretation and analysis. Applications of remote sensing include space exploration, environmental monitoring, land cover mapping, and studying the effects of deforestation. New technologies are allowing remote sensing instruments to become smaller, use more powerful computing, and observe different frequencies of light.
- The document discusses using hyperspectral remote sensing for mineral mapping. It provides background on how minerals have unique spectral signatures and defines hyperspectral imagery as image cubes with spatial and spectral data.
- Two case studies are summarized that demonstrate using techniques like atmospheric correction, MNF transformation, and spectral analysis tools like SAM and MTMF on Hyperion satellite imagery to map minerals in areas of India and Pakistan. Key minerals identified include grossularite, calcite, pyrite, andradite, and dolomite.
- The methodology involves preprocessing the hyperspectral cube, identifying endmembers, and then classifying and mapping minerals present based on their spectral properties and signatures in the imagery.
This document discusses remote sensing sensors and their characteristics. It describes how sensors are designed to record electromagnetic radiation and generate signals corresponding to energy variations of earth surface features. Imaging sensors convert EM radiation into numerical or image data. The document discusses different types of scanning sensors, including whisk broom and push broom, and covers various airborne sensors used by CIMSS including passive imagers and sounders, as well as active sensors like LIDAR.
This document provides an overview of using remote sensing and GIS in the petroleum industry. It discusses remote sensing concepts and techniques like platforms, cameras, and scanners used to collect geospatial data. It then discusses GIS and how spatial features are stored and layered with attributes for mapping and analysis. As an example, it focuses on applying these tools to study oil formation, exploration, production, and pollution in the UAE. The document aims to explain how remote sensing and GIS can help operations across the petroleum industry life cycle from exploration to environmental monitoring.
Remote sensing concept and applicationsMegha Majoe
Remote sensing involves collecting data about objects from a distance by detecting electromagnetic radiation. It has several components: an energy source, atmospheric interactions, target interactions, sensor recording, data transmission and processing, interpretation and analysis, and applications. Spatial, spectral, temporal, and radiometric resolutions characterize remote sensing data. Remote sensing is used for applications like urban planning, agriculture, natural resource management, and forestry by providing information on land use, crop growth, environmental changes, and forest resources over time.
The document provides an overview of remote sensing concepts through three lectures presented by Dr. Safaa Mohamed Hasan. The lectures cover definitions of remote sensing, sensor types, image characteristics, and resolutions including spatial, spectral, radiometric, and temporal resolutions. Geometric distortions and corrections through registration and resampling techniques are also discussed.
Lec_13_Introduction to Remote Sensing (RS)Atiqa khan
Remote sensing involves obtaining information about objects without direct contact using sensors to measure reflected or emitted electromagnetic radiation. It includes gathering data from aircraft or spacecraft using techniques ranging from aerial photographs to satellite images. The basic principle is that electromagnetic radiation interacts with the atmosphere and earth, and radiation reflected or emitted from objects is the source of remote sensing data.
AN EFFICIENT IMPLEMENTATION OF TRACKING USING KALMAN FILTER FOR UNDERWATER RO...IJCSEIT Journal
The exploration of oceans and sea beds is being made increasingly possible through the development of
Autonomous Underwater Vehicles (AUVs). This is an activity that concerns the marine community and it
must confront the existence of notable challenges. However, an automatic detecting and tracking system is
the first and foremost element for an AUV or an aqueous surveillance network. In this paper a method of
Kalman filter was presented to solve the problems of objects track in sonar images. Region of object was
extracted by threshold segment and morphology process, and the features of invariant moment and area
were analysed. Results show that the method presented has the advantages of good robustness, high
accuracy and real-time characteristic, and it is efficient in underwater target track based on sonar images
and also suited for the purpose of Obstacle avoidance for the AUV to operate in the constrained
underwater environment.
AUTOMATIC IDENTIFICATION OF CLOUD COVER REGIONS USING SURF ijcseit
Weather forecasting has become an indispensable application to predict the state of the atmosphere for a
future time based on cloud cover identification. But it generally needs the experience of a well-trained
meteorologist. In this paper, a novel method is proposed for automatic cloud cover estimation, typical to
Indian Territory Speeded Up Robust Feature Transform(SURF) is applied on the satellite images to obtain
the affine corrected images. The extracted cloud regions from the affine corrected images based on Otsu
threshold are superimposed on the artistic grids representing latitude and longitude over India. The
segmented cloud and grid composition drive a look up table mechanism to identify the cloud cover regions.
Owing to its simplicity, the proposed method processes the test images faster and provides accurate
segmentation for cloud cover regions.
This document provides an overview of remote sensing through a seminar presented by Ashwathy Babu Paul. It defines remote sensing as obtaining information about an object without physical contact through electromagnetic radiation. It describes the basic components and process of remote sensing systems including energy sources, sensor recording, transmission and processing. Various sensors and platforms are discussed along with advantages and applications in fields like agriculture, natural resource management, national security, geology, meteorology, and more. Challenges are addressed but advantages of remote sensing are said to far outweigh these.
Hyperspectral remote sensing for oil explorationJayanth Joshua
Hyperspectral remote sensing uses sensors that collect data across a wide range of electromagnetic wavelengths, with more than 100 contiguous bands that provide detailed spectral signatures. This allows identification of subtle mineral and material differences that can indicate oil and gas deposits. Seeps at the surface cause alterations detectable by hyperspectral analysis, like calcite, pyrite and clay changes. A Hydrocarbon Index highlights absorption peaks related to hydrocarbons. Classification algorithms like Spectral Angle Mapper can map hydrocarbon-bearing zones by comparing spectra to known samples. Soil tonal anomalies from bleaching or iron/clay changes also indicate subsurface structures and seepage areas for exploration.
Indian remote sensing satellite,potentials with a revolutionSamikshya Kar
Indian Remote Sensing satellites provide remote sensing services to India. Data from these satellites are used for various applications like crop production estimation, drought monitoring, land use mapping, and more. The Cartosat-1 satellite has panchromatic cameras with a spatial resolution of 2.5 meters. It collects data that is processed and analyzed to derive information about vegetation, water bodies, and other landscape features with various applications such as malaria risk mapping. Remote sensing technologies allow analyzing large regions from space and measuring energy beyond human vision to enable global monitoring.
Applications of remote sensing in geological aspectsPramoda Raj
Remote sensing uses sensors on airborne or spaceborne platforms to detect and record electromagnetic radiation from the Earth's surface. It has two main phases - data acquisition through sensors and data analysis. In geology, remote sensing is used to map lithology, structural features, and monitor hazards. It helps identify rock types and structures that can indicate mineral or oil and gas deposits. Remote sensing provides synoptic data to study geomorphology, hydrology, and other Earth processes over large areas.
This document discusses different types of remote sensing systems used in civil engineering, including optical, photogrammetric, thermal, multispectral, hyperspectral, and panchromatic systems. It provides examples and specifications of various sensors, such as MODIS, AVIRIS, IKONOS, and WorldView. The document also covers digital image formats, photogrammetry, image distortions and displacements, reference ellipsoids, relief displacement, and methods of measuring heights from aerial photographs.
This document provides information on various remote sensing platforms and Earth observing satellites. It discusses balloons, helicopters, airplanes and satellites as remote sensing platforms. It then describes different types of satellite orbits and provides details on several major Earth observing satellites including their sensors and specifications. These satellites include Landsat, SPOT, Ikonos, AVHRR, Radarsat, GOES, Meteosat, and some Indian, Japanese, European and Russian satellites.
Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+,
and EO-1 ALI sensors
Gyanesh Chander a,⁎, Brian L. Markham b, Dennis L. Helder c
a SGT, Inc. 1 contractor to the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center, Sioux Falls, SD 57198-0001, USA
b National Aeronautics and Space Administration (NASA) Goddard Space Flight Center (GSFC), Greenbelt, MD 20771, USA
c South Dakota State University (SDSU), Brookings, SD 57007, USA
Application of Remote Sensing in Civil EngineeringIEI GSC
Presentation cum talk delivered by Dr Anjana Vyas, Dean CEPT University, Ahmedabad during 31st National Convention of Civil Engineering organized by The Institution of Engineers (India) Gujarat State Center, Ahmedabad
Geographic information system and remote sensingDhiren Patel
This document provides an overview of remote sensing and geographic information systems. It discusses the history of remote sensing from early aerial photography to modern satellite systems. Both passive and active remote sensing techniques are described, along with common applications in fields like forestry, agriculture, and land use analysis. Optical, radar, and lidar remote sensing systems are outlined. The document also introduces concepts in photogrammetry, surveying, and geographic information systems, including data structures and components of GIS.
Chronological Calibration Methods for Landsat Satellite Images iosrjce
This document describes methods for chronologically calibrating Landsat satellite images to account for differences when images are taken days apart. It discusses correcting ETM+ images for scan line failures and converting digital numbers to reflectance. Two methods are proposed to remove phenological effects between Landsat 7 and 8 images taken 8 days apart: linear regression and cross-correlation. Image classification using the visible red and near infrared bands is used to validate the correction methods by comparing land cover detection in study area images.
The document discusses remote sensing satellites. It begins by defining remote sensing as obtaining information about an object through analysis of data acquired from a distance without physical contact. There are two broad categories of remote sensing based on platforms: aerial and satellite. Satellite remote sensing has advantages like continuous data acquisition and broad area coverage. Remote sensing systems are classified based on the radiation source as passive or active, and based on spectral regions as optical, thermal infrared, or microwave. Key resolutions for remote sensing include spatial, spectral, temporal, and radiometric. Common applications are land cover mapping, change detection, flood monitoring, and more. Major satellite missions discussed are Landsat, SPOT, and IKONOS.
Remote Sensing For Environmental Impact And MonitoringPaulDavidShaw
This document summarizes several remote sensing technologies used for environmental monitoring, including video, LIDAR, hyperspectral sensing, and INSAR. Video can be integrated with GPS to monitor environments. LIDAR uses light to map underwater environments up to 60m deep or 30 sq km areas on land with accuracy of 3m horizontally and 0.15m vertically. Hyperspectral sensors can identify vegetation and minerals by their light absorption characteristics across 288 wavelengths. INSAR uses phase differences from satellites to measure ground displacement with millimeter accuracy, useful for monitoring earthquakes, subsidence, and dam movement.
This document discusses data collection methods for spatial and non-spatial data. It describes different types of data like raster, vector, and attribute data. Methods of data collection include land surveying techniques like chain surveying and using total stations, as well as remote sensing using aerial photography and satellite imagery. Common data sources are provided by organizations like the Survey of India and ISRO. The document also covers topics like digitization in GIS and using software like AutoCAD.
Remote sensing uses sensors on satellites or aircraft to detect and record electromagnetic radiation from the Earth's surface in order to gather information about it without direct contact. There are six main elements of remote sensing: an energy source, radiation and the atmosphere, interaction with the target, recording by the sensor, transmission and processing, and interpretation and analysis. Applications of remote sensing include space exploration, environmental monitoring, land cover mapping, and studying the effects of deforestation. New technologies are allowing remote sensing instruments to become smaller, use more powerful computing, and observe different frequencies of light.
- The document discusses using hyperspectral remote sensing for mineral mapping. It provides background on how minerals have unique spectral signatures and defines hyperspectral imagery as image cubes with spatial and spectral data.
- Two case studies are summarized that demonstrate using techniques like atmospheric correction, MNF transformation, and spectral analysis tools like SAM and MTMF on Hyperion satellite imagery to map minerals in areas of India and Pakistan. Key minerals identified include grossularite, calcite, pyrite, andradite, and dolomite.
- The methodology involves preprocessing the hyperspectral cube, identifying endmembers, and then classifying and mapping minerals present based on their spectral properties and signatures in the imagery.
This document discusses remote sensing sensors and their characteristics. It describes how sensors are designed to record electromagnetic radiation and generate signals corresponding to energy variations of earth surface features. Imaging sensors convert EM radiation into numerical or image data. The document discusses different types of scanning sensors, including whisk broom and push broom, and covers various airborne sensors used by CIMSS including passive imagers and sounders, as well as active sensors like LIDAR.
This document provides an overview of using remote sensing and GIS in the petroleum industry. It discusses remote sensing concepts and techniques like platforms, cameras, and scanners used to collect geospatial data. It then discusses GIS and how spatial features are stored and layered with attributes for mapping and analysis. As an example, it focuses on applying these tools to study oil formation, exploration, production, and pollution in the UAE. The document aims to explain how remote sensing and GIS can help operations across the petroleum industry life cycle from exploration to environmental monitoring.
Remote sensing concept and applicationsMegha Majoe
Remote sensing involves collecting data about objects from a distance by detecting electromagnetic radiation. It has several components: an energy source, atmospheric interactions, target interactions, sensor recording, data transmission and processing, interpretation and analysis, and applications. Spatial, spectral, temporal, and radiometric resolutions characterize remote sensing data. Remote sensing is used for applications like urban planning, agriculture, natural resource management, and forestry by providing information on land use, crop growth, environmental changes, and forest resources over time.
The document provides an overview of remote sensing concepts through three lectures presented by Dr. Safaa Mohamed Hasan. The lectures cover definitions of remote sensing, sensor types, image characteristics, and resolutions including spatial, spectral, radiometric, and temporal resolutions. Geometric distortions and corrections through registration and resampling techniques are also discussed.
Lec_13_Introduction to Remote Sensing (RS)Atiqa khan
Remote sensing involves obtaining information about objects without direct contact using sensors to measure reflected or emitted electromagnetic radiation. It includes gathering data from aircraft or spacecraft using techniques ranging from aerial photographs to satellite images. The basic principle is that electromagnetic radiation interacts with the atmosphere and earth, and radiation reflected or emitted from objects is the source of remote sensing data.
Similar to Sensor Network for Landslide Monitoring With Laser Ranging System Avoiding Rainfall Influence on Laser Ranging by Means of Time Diversity and Satellite Imagery Data Based Landslide Disaster Relief
AN EFFICIENT IMPLEMENTATION OF TRACKING USING KALMAN FILTER FOR UNDERWATER RO...IJCSEIT Journal
The exploration of oceans and sea beds is being made increasingly possible through the development of
Autonomous Underwater Vehicles (AUVs). This is an activity that concerns the marine community and it
must confront the existence of notable challenges. However, an automatic detecting and tracking system is
the first and foremost element for an AUV or an aqueous surveillance network. In this paper a method of
Kalman filter was presented to solve the problems of objects track in sonar images. Region of object was
extracted by threshold segment and morphology process, and the features of invariant moment and area
were analysed. Results show that the method presented has the advantages of good robustness, high
accuracy and real-time characteristic, and it is efficient in underwater target track based on sonar images
and also suited for the purpose of Obstacle avoidance for the AUV to operate in the constrained
underwater environment.
AUTOMATIC IDENTIFICATION OF CLOUD COVER REGIONS USING SURF ijcseit
Weather forecasting has become an indispensable application to predict the state of the atmosphere for a
future time based on cloud cover identification. But it generally needs the experience of a well-trained
meteorologist. In this paper, a novel method is proposed for automatic cloud cover estimation, typical to
Indian Territory Speeded Up Robust Feature Transform(SURF) is applied on the satellite images to obtain
the affine corrected images. The extracted cloud regions from the affine corrected images based on Otsu
threshold are superimposed on the artistic grids representing latitude and longitude over India. The
segmented cloud and grid composition drive a look up table mechanism to identify the cloud cover regions.
Owing to its simplicity, the proposed method processes the test images faster and provides accurate
segmentation for cloud cover regions.
Computer model for tsunami vulnerability using sentinel 2A and SRTM images op...journalBEEI
This study aims to develop a software framework for modeling of tsunami vulnerability using DEM and Sentinel 2 images. The stages of study, are: 1) extraction Sentinel 2 images using algorithms NDVI, NDBI, NDWI, MSAVI, and MNDWI; 2) prediction vegetation indices using machine learning algorithms. 3) accuracy testing using the MSE, ME, RMSE, MAE, MPE, and MAPE; 4) spatial prediction using Kriging function and 5) modeling tsunami vulnerability indicators. The results show that in 2021 the area was dominated by vegetation density between (-0.1-0.3) with moderate to high vulnerability and risk of land use tsunami as a result of the decreasing of vegetation. The prediction results for 2021 show a low canopy density of vegetation and a high degree of land surface slope. Based on the prediction results in 2021, the study area mostly shows the existence of built-up lands with a high tsunami vulnerability risk (more than 0.1). Vegetation population had decreased to 67% from the original areas in 2017 with an area of 135 km2. Forest vegetation had decreased by 45% from 116 km2 in 2017. Land use for fisheries had increased to the area of 86 km2 from 2017 with an area of 24 km2.
The document discusses methods for characterizing the global environment using satellite data to help overcome challenges posed by weather effects on missile defense sensors. It describes adjusting infrared imagery thresholds to approximate radar observations, extracting weather event boundaries, projecting 3D shapes onto a model Earth, and using an existing satellite constellation to provide continuous coverage. The goal is to determine visibility and sensor performance to optimize sensor selection and placement for missile defense.
2013APRU_NO40-abstract-mobilePIV_YangYaoYuYao-Yu Yang
Limited particle image velocimetry (PIV) methods were used in field due to three main difficulties: 1. On-site computing device is needed; 2. An observing station is required for cameras; 3. The locating and camera calibration are complex. To overcome these problems, we used four parallel laser pointers as ground reference points and a smartphone as a computing core for calculating and demonstrating the flow field of the river. The research verified and showed the feasibility of the device. In conclusion, we developed a portable, affordable and easy-operation flow field measuring device.
This document provides information on a group assignment submitted by 16 students for their Introduction to Remote Sensing course. It includes the group number, course details, student names and registration numbers, and the assignment questions. The assignment involves describing the TRMM satellite's owner and location, orbital characteristics, identifying its onboard sensors and providing details on each sensor's specifications and functioning.
This document discusses laser ranging and LIDAR systems. It provides an overview of satellite laser ranging (SLR) which uses lasers to precisely measure distances to satellites. It then describes the key components and workings of LIDAR systems, including lasers, scanners, inertial measurement units, GPS, and how they are used to map terrain from aircraft. Sources of error in LIDAR measurements are also reviewed. The document concludes by outlining applications of LIDAR in fields such as surveying, mining, forestry, and transport.
This document describes a satellite tracking system that uses a microcontroller to track a satellite's position and correct its orbit if it drifts from its intended path. The system includes an orbital correction engine that calculates needed adjustments and a tracking processor that monitors the satellite's position over time to detect any deviations. It sends the satellite's location data to an earth station via an RS-232 interface. The microcontroller compares the data to an orbital database and determines corrections to guide the satellite back to its proper orbit. A visual basics program is used to model and simulate the satellite's movement and drift.
This document discusses using k-means clustering to detect minerals from remote sensing images. It begins with an abstract describing using k-means clustering on hyperspectral images to segment and extract features to detect minerals like giacomo. It then provides background on remote sensing, k-means clustering algorithms, and describes the giacomo mineral deposit in Peru that contains silicon dioxide and titanium dioxide. It concludes with discussing using sobel edge detection as part of the mineral detection process from remote sensing images.
Gps and its use in vehicle movement study in earthquake disaster management r...Mayur Rahangdale
What is GPS?
GPS Segments
Pseudo – Random Numbers (PRN)
Coarse acquisition (C/A) code
P code (Precision or Protected code)
P code (Precision or Protected code)
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GPS LIMITATION IN DISASTER MANAGEMENT
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Sensor Network for Landslide Monitoring With Laser Ranging System Avoiding Rainfall Influence on Laser Ranging by Means of Time Diversity and Satellite Imagery Data Based Landslide Disaster Relief
1. Kohei Arai
International Journal of Applied Sciences (IJAS), Volume (3) : Issue (1) : 2012 1
Sensor Network for Landslide Monitoring With Laser Ranging
System Avoiding Rainfall Influence on Laser Ranging by Means
of Time Diversity and Satellite Imagery Data Based Landslide
Disaster Relief
Kohei Arai arai@is.saga-u.ac.jp
Information Science Department
Saga University
Saga City, 840-8502, Japan
Abstract
Sensor network for landslide monitoring with laser ranging system is developed together with
landslide disaster relief with remote sensing satellite imagery data. Time diversity is utilized for
rainfall influence avoidance in the distance measurements between laser ranging equipment and
targets. Also automatic tie point extraction method is proposed. Experimental results show that
(1) the proposed time diversity of the laser ranging measurement does work for avoidance from
rainfall influence; (2) the proposed automatic control point extraction method does work for tie
point matching together with change detection for landslide disaster relief.
Keywords: landslide, Control Point Detection, Change Detection, Remote Sensing Satellite
Imagery Data, Time Diversity.
1. INTRODUCTION
Landslide is one of severe disaster. In order to monitor landslides, several methods have been
proposed so far. One of the conventional land slide monitoring systems is to measure the
resistance of the wire which is set at potential landslide steeply slopes. Although it is simple and
cheap system, measurement accuracy is not so good in terms of the distance between two ends
of the wire which depends on resistance. Therefore, it cannot be used for making caution for
evacuation from the potential landslide area. Other wide variety of commercial off-the-shelf
electronic and/or mechanical geotechnical and hydrologic sensors exist [1],[2]. Meanwhile,
satellite remote sensing imagery data is used for disaster relief due to landslide. It cannot be used
for landslide warning system. There are so many previously proposed methods for detection of
landslide areas such as differentiation with edge enhancing filters [3], texture feature extraction
[4], vegetation index utilized land cover change detection method [5], 3D stereo pair of images
utilized change detection method [6], etc.
The landslide monitoring method proposed here is to utilize laser ranging system as a sensor
network. The distance between the laser ranging instrument and the target which is set at
potential landslide areas can be measured with the laser ranging system. If the measured
distance is not changed then it is understood any landslide is not happened. The initial movement
of typical landslide is around a few millimeters. Therefore, it is effective is the laser ranging
system can measure a few millimeters distance changes. Measured distance data can be
transmitted from the site of where the laser ranging system is equipped to Internet terminals in
the disaster mitigation center. After that, caution for evacuation can be provided from the center
through TV broadcast stations with data broadcasting. Conventional method for landslide
monitoring uses wire current or voltage difference between two ends of the wire. Such method is
not good enough for detection of initial movement of landslide. Movement accuracy of
conventional methods is around a couple of cm while that of the proposed method is a couple of
mm. Therefore, the proposed method is useful to detect initial movement of landslide even under
2. Kohei Arai
International Journal of Applied Sciences (IJAS), Volume (3) : Issue (1) : 2012 2
a heavy rain condition. To avoid the influence scattering of laser light due to raindrops, time
diversity method is proposed.
On the other hand, landslide disaster relief can be done with satellite remote sensing data. In
order to detect landslide, changes between two satellite remote sensing imagery data which are
acquired on the different day have to be detected. Before that two satellite imagery data have to
be registered with tie point matching. In this paper, automatic tie point or control point extraction
method is also proposed. Thus geometric correction can be applied to two remote sensing
satellite imagery data. Tie points have to be extracted from two images. There are some
conventional automatic tie point extraction methods based on feature extraction [7],[8],[9]. The
conventional tie point uses line and circle features. The proposed method is simple and accurate
because it is just find "L" and "X" shaped line features extracted with Hough transformation is
used. The corners and cross points are easy to extract and accurate enough for tie point
matching.
The following section describes the proposed whole system for landslide monitoring followed by
landslide monitoring system with laser ranging system. After that landslide disaster relief method
with satellite remote sensing imagery data is proposed together with some experimental results.
2. PROPOSED SYSTEM
2.1 Proposed landslide Warning System With Laser Ranging System and Remote Sensing
Satellite
Figure.1 shows the proposed landslide warning system with laser ranging system and remote
sensing satellites. Potential landslide areas are observed by the laser ranging equipments and
remote sensing satellites. Laser ranging equipments are for detecting an initial movement of
landslide while remote sensing satellites are for landslide disaster relief. A few millimeters of the
initial movements of landslide can be detected with the laser ranging system. Figure.2 shows the
proposed landslide monitoring system with laser ranging system together with landslide warning
information transmission to the disaster mitigation center. Once initial movements is detected,
then caution or warning messages are broadcasted through the disaster mitigation center and the
TV broadcasting stations with data broadcasting system. Then the residence can be evacuated
from the landslide areas before landslide is occurred. Key issue here is distance measurement
accuracy of the laser ranging system. If the accuracy is better than a few millimeters, then it can
detect initial movement of landslide in particular in the heavy rain condition because landslide
used to be occurred in such severe weather condition. In order to overcome such influence due to
heavy rain, time diversity is employed. In the following section of experiment discusses the
accuracy in such severe condition.
FIGURE 1: Proposed landslide warning system with laser ranging system and remote sensing
satellites
3. Kohei Arai
International Journal of Applied Sciences (IJAS), Volume (3) : Issue (1) : 2012 3
FIGURE 2: Proposed landslide monitoring system with laser ranging system together with
landslide warning information transmission to the disaster mitigation center.
2.2 Proposed landslide Disaster Relief With Remote Sensing Satellite Imagery Data
Figure.3 shows examples of the remote sensing imagery data which are acquired on the different
days, (a) before and (b) after landslide occurrence. The potential landslide areas are known.
Therefore, when remote sensing satellite visits potential landslide areas, it can be acquire
imagery data. Once landslide occurs, then, remote sensing satellites acquire the imagery data
immediately after the landslide.
In order to detect change between two satellite imagery data which are acquired before and after
landslide occurrence, two images have to be matched. Before making image matching, tie points
in the images have to be extracted. Figure.4 shows the process flow of the automatic tie point
extractions. Firstly, potential landslide portion of images are extracted from two satellite images.
Then image intensity is inverted to enhance edge information. After that, contrast of the images is
enhanced followed by binarization. Then linear features are extracted with Hough transformation.
After that, longest line feature is selected. Then cross line with the detected longest line is
detected. Thus “L” or “X” character type of tie point is extracted.
By using extracted tie points, two satellite images are matched. Then difference between
matched two images which corresponds to changes between satellite images which are acquired
before and after landslide occurrence can be detected. Thus location and size of landslide can be
detected and estimated.
(a) Example of satellite image which is acquired after landslide occurrence.
(b) Example of satellite image which is acquired before landslide occurrence.
4. Kohei Arai
International Journal of Applied Sciences (IJAS), Volume (3) : Issue (1) : 2012 4
FIGURE 3: Examples of satellite imagery data which are acquired before and after landslide
occurrence.
FIGURE 4: Process flows of automatic tie point extractions.
3 EXPERIMENTS
3.1 Proposed landslide Monitoring System
Potential landslide areas are investigated and are on the web site
1
. From the site, for instance,
potential areas are retrieved with their severe degrees. Depending on the degree of severances,
three test sites in the Saga prefecture Japan are selected. Figure.5 (a), (b), and (c) shows test
sites, (1) Yamashiro town of the Imari city, (2) Ohchicho of the Karastu city, and (3) Fujicho of the
Saga city.
1
http://disapotal.gsi.go.jp/
5. Kohei Arai
International Journal of Applied Sciences (IJAS), Volume (3) : Issue (1) : 2012 5
(a) Yamashiro town of the Imari city (b) Ohchicho of the Karastu city
(c) Fujicho of the Saga city
FIGURE 5: Locations of the potential landslide areas (Red circles) ((c)Google)
Figure.6 shows the outlook of the equipments including laser ranging system installed at the
potential landslide areas. Configuration of the equipments is illustrated in Figure.7. There two
types of the equipments, Target#1 and 2. In the Target #1, there is not only laser ranging
equipment but also meteorological data acquisition system and camera system for acquire sight
scene. Target #2 is for experimental equipment which allows measurements of the critical
enforcement when landslide occurs. In the experiments with Target #2, simulated rainfall of water
is supplied from the top of the Target #2.
Figure.8 shows network configuration of the proposed landslide monitoring system. Acquired
laser ranging data and camera data as well as meteorological data area transmitted from the
sites to Internet terminal with Auto-Pilot. Under Internet, users may access to the data from the
web server through router.
(a) Yamashiro town of the Imari city (b) Ohchicho of the Karastu city
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International Journal of Applied Sciences (IJAS), Volume (3) : Issue (1) : 2012 6
(c) Fujicho of the Saga city
FIGURE 6: The Equipments installed at the potential landslide areas
FIGURE 7: Configurations for the proposed landslide monitoring system with laser ranging
equipment implemented at the test sites and experimental configuration which simulates landslide
in heavy rainfall conditions.
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International Journal of Applied Sciences (IJAS), Volume (3) : Issue (1) : 2012 7
FIGURE 8: Network configuration of the proposed landslide monitoring system.
3.2 Experiments of the Landslide Monitoring With Laser Ranging Equipments
Figure.9 shows configuration of the experiments of the landslide monitoring with laser ranging
equipments. Target with concrete is put on thick layered sand at the middle location of the slope.
From the top of the slope, simulated rainfall of water is put into the sands layer between the slope
and Target #2.
Table 1 shows the major specification of laser ranging equipment of Leica Disto A6. It can
measure distance between the laser ranging equipment and the target with ±1.5mm of accuracy
for distance between both ranges from 0.05 to 80m. Laser wavelength is 635nm. It is pulsed laser
with interval rages from 1 (ms) to 1 (sec). The most appropriate time interval has to be
determined through a trial and error. Also the most appropriate time interval for time diversity has
to be determined through experiments.
TABLE 1: Specification of laser ranging equipment of Leica Disto A6
Range 0.05-80m
accuracy ±1.5mm
Time 1ms to 1sec
FIGURE 9: Experiment configurations for simulated landslide
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International Journal of Applied Sciences (IJAS), Volume (3) : Issue (1) : 2012 8
FIGURE 10: Photo of the simulated landslide
Figure.10 shows photo of the simulated landslide. The experiments are conducted with several
slope angles. Laser raging equipment is set-up at the slope end with perpendicular to the target
depending on the slope angle. As shown in Figure.10, simulated rainfall of water is put into the
sand layer. The static friction coefficient of the sand is 0.7. Target #2 weighs about 20 Kg.
Experimental conditions are 41.672 degrees of the slope angle, 2.5mm/h of rainfall. Thus the
vertical enforcement, F=mN=14Kg. Target #2 with concrete is touched with the sand layer with
35cm by 35cm of area. For the aforementioned condition, 15 second after start to put the water
into the sand layer, Target #2 is moved down to the end of slope. Therefore, it may conclude that
the slope is destroyed, landslide occurs with 25mm/h of rainfall within 2 second. It totally depends
on the static friction coefficient of the soil, slope angle, weight of the surface layer of the land, and
rainfall rate.
3.3 Performance of the Laser Ranging Equipments and Time Diversity Effect
Leica Disto A6
2
of laser ranging equipment is tested with the different color of target and with the
different set angles, 0, 20, and 40 degrees. Experimental configuration is illustrated in Figure.11.
Also experimental results are shown in Table 2 and Figure.12. As shown in Table 2 and Figure.12,
no error is dominant frequency obviously. Also brown colored target is much accurate rather than
white target in terms of distance measurement accuracy. In these cases, most appropriate time
interval is 100ms. Under a heavy rainfall condition, laser light is scattered with raindrops.
Therefore, returned laser echo from the raindrops reaches to the laser ranging equipment much
faster than that from the target. If measurement interval is set to 1 second, then 10 measured
distances can be used to determine the distance between laser ranging equipment and target.
Thus the maximum measured distance can be used as the most appropriate distance between
both. This is called time diversity concept. Table 3 shows the effect of the time diversity. The
mean accuracy and standard deviation of measured distance with and without time diversity is
shown in Table 3. Approximately 33% of improvement of mean accuracy is confirmed for time
diversity effect.
2
http://www.leicadisto.co.uk/products/disto_a6.html
9. Kohei Arai
International Journal of Applied Sciences (IJAS), Volume (3) : Issue (1) : 2012 9
FIGURE 11: Experimental configuration for performance evaluation of laser ranging equipment of
Leica Disto A6.
FIGURE 12: Probability density function of distance measurement error.
TABLE 2: Frequency of the measured distance error in unit of mm
Target Set Angle
Frequency of the measured distance error in unit of mm
Color (degree) -3 -2 -1 0 1 2 3
Brown 0 0 0 0 59 0 0 0
20 0 0 8 45 6 0 0
40 0 0 14 43 1 1 0
White 0 0 0 9 27 14 9 0
20 0 1 5 26 13 14 0
40 0 1 6 27 18 5 2
TABLE 3: Mean and standard deviation (Std.Dev.) of distance measurement errors with/without
time diversity (TD)
without TD with TD
Mean 7.9 5.9
Std.Dev. 0.00636 0.00487
3.4 Automatic tie point extraction performance
Using Hough transformation, line features are extracted from the binarized image of remote
sensing satellite imagery data as shown in Figure.13 (a). In the extracted line features, the
longest line is selected. Then L or X character type of feature with the selected longest line
feature is detected as shown in Figure.13 (b).
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International Journal of Applied Sciences (IJAS), Volume (3) : Issue (1) : 2012 10
Thus image matching can be done using a plenty of tie points detected from the four corners of
the image. Also it makes image matching by using manually extracted tie points through human
perception as shown in Figure.14 (a). In this case, user appoints tie points by pixel by pixel basis
in the image of Figure.14 (a). Then the difference between two satellite images which are
acquired on before and after landslide occurrence is derived as shown in Figure.14 (b). After all,
changes during land slide can be extracted as shown in Figure.14 (c). Red colored portions of
Figure.14 (c) is actual landslide area.
(a) Extracted line features (b) Detected L or X character type of feature
FIGURE 13: Tie point detection
(a)Screen shot image for manual extraction (b)Difference between two satellite images of tie
points. which are acquired on before and after
landslide occur
11. Kohei Arai
International Journal of Applied Sciences (IJAS), Volume (3) : Issue (1) : 2012 11
(c)Detected landslide areas (Red colored portion)
FIGURE 14: Manual tie point extraction, detected difference between two satellite images and
extracted landslide areas with the proposed method.
4. CONCLUSION
It is found that the proposed landslide monitoring system with laser ranging equipment and the
proposed landslide disaster relief with remote sensing satellite imagery data does work so well. In
particular, laser ranging equipment allows around 2-3mm of distance measurement accuracy for
the distance between laser ranging equipment and target of 20m. Also around 30% improvement
of time diversity effect is confirmed in comparison to the conventional method without time
diversity. This landslide monitoring with laser ranging equipment of sensor network is original
system of this paper. Landslide usually occurs under a heavy rainfall condition. Heavy rain affects
to laser ranging through scattering of laser light due to raindrops. Therefore, some
countermeasure has to be taken into account. Time diversity (extract the longest distance
measurement data during a relatively long period of measuring time) is adopted. As shown in
Fig.12 and Table 2 and 3, 2-3mm of distance measurement accuracy is achieved by the
proposed method. 2-3mm of accuracy is good enough for initial stage of landslide detection.
Conventional method for landslide monitoring is based on wire current and/or voltage difference
between two ends of the wire. The accuracy is around a couple of cm. Therefore, it is not useful
to detect initial movement of the land before landslide.
On the other hand, automatic tie point extractions and change detection between before and after
landslide occurs satellite images also work so well. From extracted image portion, tie points which
are appropriate for image matching which are acquired on the different days, before and after
landslide occur can be extracted. Then change detection, cross comparison between two images
can be done perfectly. Land cover change implies landslide occur. Therefore, landslide is relieved
with two satellite images. Hazard map created by the ministry of land, infrastructure,
transportation and tourism shows potential landslide areas in Japan. Therefore, satellite imagery
database for the potential areas can be created. Only thing we have to do is to detect changes
between before (in the satellite imagery database) and after (currently acquired satellite image)
landslide occurs. Two images have to be matched for change detections. By using tie points
between two images based on the proposed method of tie points extraction, a good enough
matching accuracy (a sub-pixel order) can be achieved. Tie points which are situated at the
corner of "L" character type or crossing points of "X" character type can be detected with line
features extracted based on Hough transformation are original feature of this paper. The
proposed method is validated with 10m spatial resolution of ALOS/AVNIR-2 imagery data. As
shown in Fig.14 (c), landslide areas are detected perfectly. It is also confirmed that the proposed
method is superior to the conventional method for tie point extraction which is based on feature
extraction methods.
12. Kohei Arai
International Journal of Applied Sciences (IJAS), Volume (3) : Issue (1) : 2012 12
5.ACKNOWLEDGEMENT
This research is founded by the ministry of education, culture, sport, science and technology of
Japan as a part of “Ultra small earth observation satellite of Kyushu, so called QSAT-EOS
development”. In this connection, author would like to thank to all the staff of the ministry. Also
author would like to thank to Map Station Co. Ltd. for their experimental effort to construct
landslide monitoring system with laser ranging equipment.
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