Image Analysis, Classification and Change Detection in Remote Sensing 3e

Features

• Utilizes computer codes to illustrate the methods and algorithms discussed
• Presents new material on SAR remote sensing data classification
• Discusses cloud computing possibilities and includes updated material on GPU programming
• Includes statistical change detection techniques for polarimetric synthetic aperture radar data
• Offers advance materials in the appendices

Summary

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.