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Image Analysis, Classification and Change Detection in Remot

Image Analysis, Classification and Change Detection in Remot - Morton John Canty

Image Analysis, Classification and Change Detection in Remot

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,



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1040.40Lei

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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,



Citeste mai mult

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