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Author: Kee-Tae Kim
Publisher:
ISBN:
Category : Remote sensing
Languages : en
Pages :
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Book Description
Abstract: Declassified Intelligence Satellite Photograph(DISP) data are important resources for measuring the geometry of the coastline of Antarctica. By using the state-of-art digital imaging technology, bundle block triangulation based on tie points and control points derived from a RADARSAT-1 Synthetic Aperture Radar(SAR) image mosaic and Ohio State University(OSU) Antarctic digital elevation model(DEM), the individual DISP images were accurately assembled into a map quality mosaic of Antarctica as it appeared in 1963. The new map is one of important benchmarks for gauging the response of the Antarctic coastline to changing climate. Automated coastline extraction algorithm design is the second theme of this dissertation. At the pre-processing stage, an adaptive neighborhood filtering was used to remove the film-grain noise while preserving edge features. At the segmentation stage, an adaptive Bayesian approach to image segmentation was used to split the DISP imagery into its homogenous regions, in which the fuzzy c-means clustering(FCM) technique and Gibbs random field(GRF) model were introduced to estimate the conditional and prior probability density functions. A Gaussian mixture model was used to estimate the reliable initial values for the FCM technique. At the post-processing stage, image object formation and labeling, removal of noisy image objects, and vectorization algorithms were sequentially applied to segmented images for extracting a vector representation of coastlines. Results were presented that demonstrate the effectiveness of the algorithm in segmenting the DISP data. In the cases of cloud cover and little contrast scenes, manual editing was carried out based on intermediate image processing and visual inspection in comparison of old paper maps. Through a geographic information system(GIS), the derived DISP coastline data were integrated with earlier and later data to assess continental scale changes in the Antarctic coast. Computing the area of ma jor Antarctic ice shelves between 1963 and 1997, we found that the net loss was approximately 0.8% and ice shelves retreated mostly between DISP and Scientific Committee Antarctic Research (SCAR) Antarctic Digital Database (ADD). In addition, over the 56-years (1947-present) observations on Pine Island Glacier, we found that the retreat rate has been approximately -10 " 65 m/yr.
Author: Kee-Tae Kim
Publisher:
ISBN:
Category : Remote sensing
Languages : en
Pages :
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Book Description
Abstract: Declassified Intelligence Satellite Photograph(DISP) data are important resources for measuring the geometry of the coastline of Antarctica. By using the state-of-art digital imaging technology, bundle block triangulation based on tie points and control points derived from a RADARSAT-1 Synthetic Aperture Radar(SAR) image mosaic and Ohio State University(OSU) Antarctic digital elevation model(DEM), the individual DISP images were accurately assembled into a map quality mosaic of Antarctica as it appeared in 1963. The new map is one of important benchmarks for gauging the response of the Antarctic coastline to changing climate. Automated coastline extraction algorithm design is the second theme of this dissertation. At the pre-processing stage, an adaptive neighborhood filtering was used to remove the film-grain noise while preserving edge features. At the segmentation stage, an adaptive Bayesian approach to image segmentation was used to split the DISP imagery into its homogenous regions, in which the fuzzy c-means clustering(FCM) technique and Gibbs random field(GRF) model were introduced to estimate the conditional and prior probability density functions. A Gaussian mixture model was used to estimate the reliable initial values for the FCM technique. At the post-processing stage, image object formation and labeling, removal of noisy image objects, and vectorization algorithms were sequentially applied to segmented images for extracting a vector representation of coastlines. Results were presented that demonstrate the effectiveness of the algorithm in segmenting the DISP data. In the cases of cloud cover and little contrast scenes, manual editing was carried out based on intermediate image processing and visual inspection in comparison of old paper maps. Through a geographic information system(GIS), the derived DISP coastline data were integrated with earlier and later data to assess continental scale changes in the Antarctic coast. Computing the area of ma jor Antarctic ice shelves between 1963 and 1997, we found that the net loss was approximately 0.8% and ice shelves retreated mostly between DISP and Scientific Committee Antarctic Research (SCAR) Antarctic Digital Database (ADD). In addition, over the 56-years (1947-present) observations on Pine Island Glacier, we found that the retreat rate has been approximately -10 " 65 m/yr.
Author: R. S. Hawkins
Publisher:
ISBN:
Category : Image processing
Languages : en
Pages : 70
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Book Description
A new approach to the analysis of satellite imagery is presented. The central part of this approach is an algorithm which compresses information stored in the ordinary six or eight bits per picture element into only one bit. The quality of this compression is demonstrated by examples of its application to high resolution visual imagery. Both visual inspection and rms difference criterion are used for this evaluation. There are four objectives of this report which are: to review the status of processing techniques which remove redundant information, to show the need for redundance reduction in the processing of satellite images, to present the development of an algorithm for reducing it, and to show results obtained by application of the algorithm to visual imagery. Also, comments are made on needed developments of the technique and its potential application to problems of analysis of satellite imagery data. (Author).
Author: Lizy Abraham
Publisher: LAP Lambert Academic Publishing
ISBN: 9783659303227
Category :
Languages : en
Pages : 88
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Book Description
Automatic and unsupervised detection and extraction of objects from satellite images has been an important research topic in the field of photogrammetry and remote sensing in recent years. Several commercial satellites with high-resolution imaging capability and which can provide multispectral images were launched into different orbits around Earth. High resolution satellite imagery is being increasingly employed for large-scale topographic mapping, and especially for updating Geographic Information System (GIS) databases. Satellite images have inhomogeneous properties that make it hard to develop generic algorithms for object detection. The image quality may vary widely depending on resolution, sensor type, sun elevation and azimuth angles. This book analyzes structural feature extraction from satellite images which exclusively discusses algorithms for roads, buildings and bridges. Buildings may have complicated non-linear structures and can be occluded by other buildings or trees. So, as a supportive tool, detection and elimination of shadows and clouds are also considered. The algorithms developed are simulated and competitive results are obtained for the methods.
Author:
Publisher:
ISBN:
Category : Electronic dissertations
Languages : en
Pages :
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Book Description
Recently available high-resolution commercial satellite imagery provides an important new data source for remote sensing applications. Automated feature extraction (AFE) techniques can assist human analysts by rapidly locating geospatial information and have the potential to significantly reduce the amount of time to process and analyze geospatial data. In this research, we have designed and developed systems for automatic extraction of man-made objects (roads, buildings and vehicles) from high-resolution satellite imagery. We conclude that AFE can be greatly enriched and improved by multiinformation fusion and/or multi-cue integration. For road extraction and building extraction respectively, multiple detectors were developed and the extraction performance was greatly improved using multi-detector fusion from different information sources. For vehicle detection, a GIS road vector layer was used to incorporate contextual information and an implicit vehicle model including spectral and spatial characteristics was learned by a morphological shared-weight neural network. An important characteristic of our research on road and building extraction is that our extraction strategies are fully automated with only a few preset parameters. Compared with related research in these areas, the performance evaluations of our extraction systems are among the highest statistical values reported in literature thus far.
Author: Armin Gruen
Publisher: Birkhäuser
ISBN: 3034889062
Category : Technology & Engineering
Languages : en
Pages : 398
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Book Description
Advancements in digital sensor technology, digital image analysis techniques, as well as computer software and hardware have brought together the fields of computer vision and photogrammetry, which are now converging towards sharing, to a great extent, objectives and algorithms. The potential for mutual benefits by the close collaboration and interaction of these two disciplines is great, as photogrammetric know-how can be aided by the most recent image analysis developments in computer vision, while modern quantitative photogrammetric approaches can support computer vision activities. Devising methodologies for automating the extraction of man-made objects (e.g. buildings, roads) from digital aerial or satellite imagery is an application where this cooperation and mutual support is already reaping benefits. The valuable spatial information collected using these interdisciplinary techniques is of improved qualitative and quantitative accuracy. This book offers a comprehensive selection of high-quality and in-depth contributions from world-wide leading research institutions, treating theoretical as well as implementational issues, and representing the state-of-the-art on this subject among the photogrammetric and computer vision communities.
Author: Mohammad Awrangjeb
Publisher: MDPI
ISBN: 3039283820
Category : Science
Languages : en
Pages : 442
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Book Description
Building extraction from remote sensing data plays an important role in urban planning, disaster management, navigation, updating geographic databases, and several other geospatial applications. Even though significant research has been carried out for more than two decades, the success of automatic building extraction and modeling is still largely impeded by scene complexity, incomplete cue extraction, and sensor dependency of data. Most recently, deep neural networks (DNN) have been widely applied for high classification accuracy in various areas including land-cover and land-use classification. Therefore, intelligent and innovative algorithms are needed for the success of automatic building extraction and modeling. This Special Issue focuses on newly developed methods for classification and feature extraction from remote sensing data for automatic building extraction and 3D
Author: Surekha Borra
Publisher: Springer
ISBN: 9811364249
Category : Technology & Engineering
Languages : en
Pages : 110
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Book Description
Thanks to recent advances in sensors, communication and satellite technology, data storage, processing and networking capabilities, satellite image acquisition and mining are now on the rise. In turn, satellite images play a vital role in providing essential geographical information. Highly accurate automatic classification and decision support systems can facilitate the efforts of data analysts, reduce human error, and allow the rapid and rigorous analysis of land use and land cover information. Integrating Machine Learning (ML) technology with the human visual psychometric can help meet geologists’ demands for more efficient and higher-quality classification in real time. This book introduces readers to key concepts, methods and models for satellite image analysis; highlights state-of-the-art classification and clustering techniques; discusses recent developments and remaining challenges; and addresses various applications, making it a valuable asset for engineers, data analysts and researchers in the fields of geographic information systems and remote sensing engineering.
Author: E.P. Baltsavias
Publisher: CRC Press
ISBN: 9789058092526
Category : Computers
Languages : en
Pages : 436
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Book Description
This work is a collection of papers from the world's leading research groups in the field of automatic extraction of objects, especially buildings and roads, from aerial and space imagery, including new sensors like SAR and lidar.
Author: Elan Sharghi
Publisher:
ISBN: 9781303024009
Category :
Languages : en
Pages : 36
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Book Description
The development of sophisticated remote sensing sensors is rapidly increasing, and the vast amount of satellite imagery collected is too much to be analyzed manually by a human image analyst. It has become necessary for a tool to be developed to automate the job of an image analyst. This tool would need to intelligently detect and classify objects of interest through computer vision algorithms. Existing software called the Rapid Image Exploitation Resource (RAPIER) was designed by engineers at Space and Naval Warfare Systems Center Pacific (SSC PAC) to perform exactly this function. This software automatically searches for anomalies in the ocean and reports the detections as a possible ship object. However, if the image contains a high percentage of cloud coverage, a high number of false positives are triggered by the clouds. The focus of this thesis is to explore various feature extraction and classification methods to accurately distinguish clouds from ship objects. An examination of a texture analysis method, line detection using the Hough transform, and edge detection using wavelets are explored as possible feature extraction methods. The features are then supplied to a K-Nearest Neighbors (KNN) or Support Vector Machine (SVM) classifier. Parameter options for these classifiers are explored and the optimal parameters are determined.
Author: Deren Li
Publisher: Springer Science & Business Media
ISBN: 1441900500
Category : Science
Languages : en
Pages : 555
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Book Description
Earth Observation interacts with space, remote sensing, communication, and information technologies, and plays an increasingly significant role in Earth related scientific studies, resource management, homeland security, topographic mapping, and development of a healthy, sustainable environment and community. Geospatial Technology for Earth Observation provides an in-depth and broad collection of recent progress in Earth observation. Contributed by leading experts in this field, the book covers satellite, airborne and ground remote sensing systems and system integration, sensor orientation, remote sensing physics, image classification and analysis, information extraction, geospatial service, and various application topics, including cadastral mapping, land use change evaluation, water environment monitoring, flood mapping, and decision making support. Geospatial Technology for Earth Observation serves as a valuable training source for researchers, developers, and practitioners in geospatial science and technology industry. It is also suitable as a reference book for upper level college students and graduate students in geospatial technology, geosciences, resource management, and informatics.
