Classification methods for remotely sensed data
著者
書誌事項
Classification methods for remotely sensed data
CRC Press, c2009
2nd ed
注記
Includes bibliographical references (p. 317-347) and index
内容説明・目次
内容説明
Since the publishing of the first edition of Classification Methods for Remotely Sensed Data in 2001, the field of pattern recognition has expanded in many new directions that make use of new technologies to capture data and more powerful computers to mine and process it. What seemed visionary but a decade ago is now being put to use and refined in commercial applications as well as military ones.
Keeping abreast of these new developments, Classification Methods for Remotely Sensed Data, Second Edition provides a comprehensive and up-to-date review of the entire field of classification methods applied to remotely sensed data. This second edition provides seven fully revised chapters and two new chapters covering support vector machines (SVM) and decision trees. It includes updated discussions and descriptions of Earth observation missions along with updated bibliographic references. 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.
This cutting-edge resource:
Presents a number of approaches to solving the problem of allocation of data to one of several classes
Covers potential approaches to the use of decision trees
Describes developments such as boosting and random forest generation
Reviews lopping branches that do not contribute to the effectiveness of the decision trees
Complete with detailed comparisons, experimental results, and discussions for each classification method introduced, this book will bolster the work of researchers and developers by giving them access to new developments. It also provides students with a solid foundation in remote sensing data classification methods.
目次
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.
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