Image Analysis, Classification and Change Detection in Remote Sensing: With Algorithms for ENVI/IDL and Python, Third Edition

Image Analysis, Classification and Change Detection in Remote Sensing: With Algorithms for ENVI/IDL and Python, Third Edition
Author: Morton J. Canty
Publisher Finelybook 出版社:CRC Press; 3rd edition (1 Aug. 2014)
Language 语言:English
pages 页数:576 pages
ISBN-10 书号:1466570377
ISBN-13 书号:9781466570375

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 Author: tightly interweaving theory, algorithms, and computer codes.

See What’s New in the Third Edition 版次:

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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