Classification Methods for Remotely Sensed Data

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Summary

Remote sensing is an integral part of geography, GIS and cartography, used by academics in the field and professionals in all sorts of occupations. The 1990s saw the development of a range of new methods of classifying remote sensing images and data, both optical imaging and microwave imaging. This comprehensive survey of the various techniques pulls together information from a range of sources and sets it in the context of the basic principles. There is an emphasis on new methods, including neural networks (especially artificial neural networks), fuzzy theory, texture and quantization, and the use of Markov random fields. Students in GIS and remote sensing should find this an essential read when learning about and dealing with new developments in the field. It is concise and accessible and the authors conclude with coverage of the state-of-the-art topics of multisource data analysis, evidential reasoning and genetic algorithms. Including a full color section and basic remote sensing theory, this book will prove invaluable for advanced undergraduate students and graduates/researchers in the field. There is very little published in this field yet, and there is distinct need for such an analysis of this fast-growing area.

Table of Contents

Remote Sensing in the Optical and Microwave Regions. Pattern Recognition Principles. Pattern Recognition Using Artificial Neural Networks. Methods Based on Fuzzy Set Theory. Texture Quantization. Modeling Context Using Markov Random Fields. Multi-source Classification.