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Image Analysis, Classification and Change Detection in Remote Sensing - With Algorithms for Python, Fourth Edition (Hardcover, 4th edition)
Loot Price: R3,936
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Image Analysis, Classification and Change Detection in Remote Sensing - With Algorithms for Python, Fourth Edition (Hardcover, 4th edition)
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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, 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.
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