Image analysis, classification and change detection in remote sensing with algorithms for ENVI/IDL and Python
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.
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Boca Raton, Florida CRC Press Taylor and Francis Group
2014
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KOHA-OAI-ECOSUR:584522020-11-25T17:04:58ZImage analysis, classification and change detection in remote sensing with algorithms for ENVI/IDL and Python Canty, Morton J. autor/a textBoca Raton, Florida CRC Press Taylor and Francis Group2014spaIntroduces 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.Incluye bibliografía e índicePreface to the First Edition.. Preface to the Second Edition.. Preface to the Third Edition.. List of Figures.. Program Listings.. 1. Images, Arrays, and Matrices.. 2. Image Statistics.. 3. Transformations.. 4. Filters, Kernels, and Fields.. 5. Image Enhancement and Correction.. 6. Supervised Classification: Part 1.. 7. Supervised Classification: Part 2.. 8. Unsupervised Classification.. 9. Change Detection.. A. Mathematical Tools.. B. Efficient Neural Network Training Algorithms.. C. ENVI Extensions in IDL.. D. Python Scripts.. Mathematical Notation.. References.. IndexIntroduces 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.Sensores remotosProcesamiento de imágenesURN:ISBN:1466570377URN:ISBN:9781466570375 |
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Sensores remotos Procesamiento de imágenes Sensores remotos Procesamiento de imágenes Canty, Morton J. autor/a Image analysis, classification and change detection in remote sensing with algorithms for ENVI/IDL and Python |
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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. |
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Texto |
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Sensores remotos Procesamiento de imágenes |
author |
Canty, Morton J. autor/a |
author_facet |
Canty, Morton J. autor/a |
author_sort |
Canty, Morton J. autor/a |
title |
Image analysis, classification and change detection in remote sensing with algorithms for ENVI/IDL and Python |
title_short |
Image analysis, classification and change detection in remote sensing with algorithms for ENVI/IDL and Python |
title_full |
Image analysis, classification and change detection in remote sensing with algorithms for ENVI/IDL and Python |
title_fullStr |
Image analysis, classification and change detection in remote sensing with algorithms for ENVI/IDL and Python |
title_full_unstemmed |
Image analysis, classification and change detection in remote sensing with algorithms for ENVI/IDL and Python |
title_sort |
image analysis, classification and change detection in remote sensing with algorithms for envi/idl and python |
publisher |
Boca Raton, Florida CRC Press Taylor and Francis Group |
publishDate |
2014 |
work_keys_str_mv |
AT cantymortonjautora imageanalysisclassificationandchangedetectioninremotesensingwithalgorithmsforenviidlandpython |
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