Classification Methods for Remotely Sensed Data
Paul Mather, Brandt Tso
This comprehensive emphasizes new methods involved in the extraction of thematic information from remotely sensed images, including neural networks (especially artificial neural networks), fuzzy theory, texture and quantization, and the use of Markov random fields. 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, Classification Methods for Remotely Sensed Data will prove invaluable for advanced undergraduate students and graduates/researchers in the field.
