Single Pixel Target Detection Using Multispectral Background Changes PDF Download
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Author: Alfredo Lugo
Publisher:
ISBN:
Category : Image processing
Languages : en
Pages : 154
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Book Description
"Possible methods to help a remote sensing analyst to find a static or moving single pixel target over vast areas of terrain were examined in this work. Specifically, the research deals with the particular problem of how to find these targets using multiple images of the same area that were collected with the same multispectral (6 bands) imaging sensor but with a background that changes between images. For this, hyperspectral quadratic covariance-based anomalous change detection algorithms were investigated to see if they could be used with multispectral data to find a moving target. In addition, a new method based on change vector analysis was developed to find a static target. In the case of the moving target problem, the performance of the Chronochrome, Covariance Equalization, and the Hyperbolic anomalous change detection algorithms were compared relative to each other and to a straight target detection algorithm. In addition, modifications to the covariance-based algorithms were developed that improved the results. For the static target case, various multispectral images were 'layer stacked' together. Then, the Spectral Matched Filter hyperspectral target detection algorithm was applied on these data cubes to explore if this method could help separate a real target from false alarms obtained when simply running a target detection algorithm on a multispectral data cube. The analysis demonstrated that a significant reduction in the number of false alarms can be obtained with these methods when compared to traditional Spectral Matched Filter (SMF) algorithm to find either static or dynamic single pixel targets of interest. In addition, the analysis shows the limitations and behavior of these methods under some of the issues normally encountered in remote sensing imaging. Overall, it was demonstrated that periodic multispectral imagery collections over a wide area can be very useful to find targets of interest."--Abstract.
Author: Alfredo Lugo
Publisher:
ISBN:
Category : Image processing
Languages : en
Pages : 154
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Book Description
"Possible methods to help a remote sensing analyst to find a static or moving single pixel target over vast areas of terrain were examined in this work. Specifically, the research deals with the particular problem of how to find these targets using multiple images of the same area that were collected with the same multispectral (6 bands) imaging sensor but with a background that changes between images. For this, hyperspectral quadratic covariance-based anomalous change detection algorithms were investigated to see if they could be used with multispectral data to find a moving target. In addition, a new method based on change vector analysis was developed to find a static target. In the case of the moving target problem, the performance of the Chronochrome, Covariance Equalization, and the Hyperbolic anomalous change detection algorithms were compared relative to each other and to a straight target detection algorithm. In addition, modifications to the covariance-based algorithms were developed that improved the results. For the static target case, various multispectral images were 'layer stacked' together. Then, the Spectral Matched Filter hyperspectral target detection algorithm was applied on these data cubes to explore if this method could help separate a real target from false alarms obtained when simply running a target detection algorithm on a multispectral data cube. The analysis demonstrated that a significant reduction in the number of false alarms can be obtained with these methods when compared to traditional Spectral Matched Filter (SMF) algorithm to find either static or dynamic single pixel targets of interest. In addition, the analysis shows the limitations and behavior of these methods under some of the issues normally encountered in remote sensing imaging. Overall, it was demonstrated that periodic multispectral imagery collections over a wide area can be very useful to find targets of interest."--Abstract.
Author:
Publisher:
ISBN:
Category : Computer algorithms
Languages : en
Pages : 270
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Book Description
Author:
Publisher:
ISBN:
Category : Computer algorithms
Languages : en
Pages : 804
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Book Description
Author: Morton J. Canty
Publisher: CRC Press
ISBN: 1466570377
Category : Mathematics
Languages : en
Pages : 575
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Book Description
Image Analysis, Classification and Change Detection in Remote Sensing: With Algorithms for ENVI/IDL and Python, Third Edition introduces techniques used in the processing of remote sensing digital imagery. It emphasizes the development and implementation of statistically motivated, data-driven techniques. The author achieves this by tightly interweaving theory, algorithms, and computer codes. See What’s New in the Third Edition: Inclusion of extensive code in Python, with a cloud computing example New material on synthetic aperture radar (SAR) data analysis New illustrations in all chapters Extended theoretical development The material is self-contained and illustrated with many programming examples in IDL. The illustrations and applications in the text can be plugged in to the ENVI system in a completely transparent fashion and used immediately both for study and for processing of real imagery. The inclusion of Python-coded versions of the main image analysis algorithms discussed make it accessible to students and teachers without expensive ENVI/IDL licenses. Furthermore, Python platforms can take advantage of new cloud services that essentially provide unlimited computational power. The book covers both multispectral and polarimetric radar image analysis techniques in a way that makes both the differences and parallels clear and emphasizes the importance of choosing appropriate statistical methods. Each chapter concludes with exercises, some of which are small programming projects, intended to illustrate or justify the foregoing development, making this self-contained text ideal for self-study or classroom use.
Author: Emmett J. Ientilucci
Publisher:
ISBN:
Category : Computer algorithms
Languages : en
Pages : 402
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Book Description
"This thesis develops a new hybrid target detection algorithm called the Physics Based-Structured InFeasibility Target-detector (PB-SIFT) which incorporates Physics Based Modeling (PBM) along with a new Structured Infeasibility Projector (SIP) metric. Traditional matched filters are susceptible to leakage or false alarms due to bright or saturated pixels that appear target-like to hyperspectral detection algorithms but are not truly target. This detector mitigates against such false alarms. More often than not, detection algorithms are applied to atmospherically compensated hyperspectral imagery. Rather than compensate the imagery, we take the opposite approach by using a physics based model to generate permutations of what the target might look like as seen by the sensor in radiance space. The development and status of such a method is presented as applied to the generation of target spaces. The generated target spaces are designed to fully encompass image target pixels while using a limited number of input model parameters. Evaluation of such target spaces shows that they can reproduce a HYDICE image target pixel spectrum to less than 1% RMS error (equivalent reflectance) in the visible and less than 6% in the near IR. Background spaces are modeled using a linear subspace (structured) approach characterized by basis vectors found by using the maximum distance method (MaxD). The SIP is developed along with a Physics Based Orthogonal Projection Operator (PBosp) which produces a 2 dimensional decision space. Results from the HYDICE FR I data set show that the physics based approach, along with the PB-SIFT algorithm, can out perform the Spectral Angle Mapper (SAM) and Spectral Matched Filter (SMF) on both exposed and fully concealed man-made targets found in hyperspectral imagery. Furthermore, the PB-SIFT algorithm performs as good (if not better) than the Mixture Tuned Matched Filter (MTMF)"--Abstract.
Author:
Publisher:
ISBN:
Category : Computer algorithms
Languages : en
Pages : 622
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Book Description
Author: Henning Heiselberg
Publisher: MDPI
ISBN: 3039436090
Category : Science
Languages : en
Pages : 286
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Book Description
The Special Issue entitled “Remote Sensing in Vessel Detection and Navigation” comprises 15 articles on many topics related to remote sensing with navigational sensors. The sequence of articles included in this Special Issue is in line with the latest scientific trends. The latest developments in science, including artificial intelligence, were used. It can be said that navigation and vessel detection remain important and hot topics, and a lot of work will continue to be done worldwide. New techniques and methods for analyzing and extracting information from navigational sensors and data have been proposed and verified. Some of these will spark further research, and some are already mature and can be considered for industrial implementation and development.
Author: Antoni Rogalski
Publisher: CRC Press
ISBN: 1351984764
Category : Technology & Engineering
Languages : en
Pages : 1044
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Book Description
This new edition of Infrared and Terahertz Detectors provides a comprehensive overview of infrared and terahertz detector technology, from fundamental science to materials and fabrication techniques. It contains a complete overhaul of the contents including several new chapters and a new section on terahertz detectors and systems. It includes a new tutorial introduction to technical aspects that are fundamental for basic understanding. The other dedicated sections focus on thermal detectors, photon detectors, and focal plane arrays.
Author: Antonio Rogalski
Publisher: CRC Press
ISBN: 1420076728
Category : Science
Languages : en
Pages : 900
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Book Description
Completely revised and reorganized while retaining the approachable style of the first edition, Infrared Detectors, Second Edition addresses the latest developments in the science and technology of infrared (IR) detection. Antoni Rogalski, an internationally recognized pioneer in the field, covers the comprehensive range of subjects necessary to un
Author: Morton John Canty
Publisher: CRC Press
ISBN: 0429875355
Category : Technology & Engineering
Languages : en
Pages : 508
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Book Description
Image Analysis, Classification and Change Detection in Remote Sensing: With Algorithms for Python, Fourth Edition, is focused on the development and implementation of statistically motivated, data-driven techniques for digital image analysis of remotely sensed imagery and it features a tight interweaving of statistical and machine learning theory of algorithms with computer codes. It develops statistical methods for the analysis of optical/infrared and synthetic aperture radar (SAR) imagery, including wavelet transformations, kernel methods for nonlinear classification, as well as an introduction to deep learning in the context of feed forward neural networks. New in the Fourth Edition: An in-depth treatment of a recent sequential change detection algorithm for polarimetric SAR image time series. The accompanying software consists of Python (open source) versions of all of the main image analysis algorithms. Presents easy, platform-independent software installation methods (Docker containerization). Utilizes freely accessible imagery via the Google Earth Engine and provides many examples of cloud programming (Google Earth Engine API). Examines deep learning examples including TensorFlow and a sound introduction to neural networks, Based on the success and the reputation of the previous editions and compared to other textbooks in the market, Professor Canty’s fourth edition differs in the depth and sophistication of the material treated as well as in its consistent use of computer codes to illustrate the methods and algorithms discussed. It is self-contained and illustrated with many programming examples, all of which can be conveniently run in a web browser. Each chapter concludes with exercises complementing or extending the material in the text.
