Advanced Image Processing Techniques for Remotely Sensed Hyperspectral Data

About this book

The main objective of this book is to apprise the reader of the use of a number of tools and techniques for a variety of image processing tasks, namely Independent Component Analysis (ICA), Mutual Information (MI), Markov Random Field (MRF) Models and Support Vector Machines (SVM). Typical applications considered are feature extraction, image classification, image fusion and change detection. The book also treats a number of experimental examples based on a variety of remote sensors. The utility of the book will be highly appreciated by academicians and R & D professionals, who are involved in current research in the area of hyperspectral imaging, as well as by professional remote-sensing data users such as geologists, hydrologists, environmental scientists, civil engineers and computer scientists.

Contents

Hyperspectral Sensors and Applications.- Overview of Image Processing.- Mutual Information: A Similarity Measure for Intensity Based Image Registration.- Independent Component Analysis.- Support Vector Machines.- Markov Random Field Models.- Applications: MI Based Registration of Multi-Sensor and Multi-Temporal Images.- Feature Extraction from Hyperspectral Data Using ICA.- Hyperspectral Classification using ICA Based Mixture Model.- Support Vector Machines for Classification of Multi- and Hyperspectral Data.- An MRF Model Based Approach for Sub-pixel Mapping from Hyperspectral Data.- Image Change Detection and Fusion Using MRF Models.

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