Paul Mather, Brandt Tso

Classification Methods for Remotely Sensed Data (eBook, PDF)

• Format: PDF

Produktbeschreibung

Classification Methods for Remotely Sensed Data, Second Edition provides a comprehensive review of the field of classification methods applied to remotely sensed data. This edition provides seven fully revised chapters, and two new chapters covering support vector machines (SVM) and decision trees. The book also includes updated bibliographic references and updated discussions and descriptions of Earth observation missions. After an introduction to the basics, the text provides a detailed discussion of different approaches to image classification, including maximum likelihood, fuzzy sets, and artificial neural networks.

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Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 376
• Erscheinungstermin: 19. April 2016
• Englisch
• ISBN-13: 9781420090741
• Artikelnr.: 42123293

Inhaltsangabe

Remote Sensing in the Optical and Microwave Regions. Pattern Recognition Principles. Artificial Neural Networks. Support Vector Machines. Methods Based on Fuzzy Set Theory. Decision Trees. Texture Quantization. Modeling Context Using Markov Random Fields. Multisource Classification. Bibliography. Index.

Rezensionen

... a very useful addition to the shelf of anyone engaged in remote sensing image analysis. It is suitable for both students and practitioners.
-Michael Collins, in GEOMATICA, Vol. 63, No. 4

... a very useful addition to the shelf of anyone engaged in remote sensing image analysis. It is suitable for both students and practitioners.
-Michael Collins, in GEOMATICA, Vol. 63, No. 4