Advanced Image Processing Techniques for Remotely Sensed Hyperspectral Data

The first of its kind, this book reviews image processing tools and techniques including Independent Component Analysis, Mutual Information, Markov Random Field Models and Support Vector Machines. The book also explores a number of experimental examples based on a variety of remote sensors. The book will be useful to people involved in hyperspectral imaging research, as well as by remote-sensing data like geologists, hydrologists, environmental scientists, civil engineers and computer scientists.

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.