Classification Methods for Remotely Sensed Data Contributor(s): Mather, Paul (Author), Tso, Brandt (Author) |
|
ISBN: 1420090720 ISBN-13: 9781420090727 Publisher: CRC Press OUR PRICE: $171.00 Product Type: Hardcover - Other Formats Published: May 2009 Annotation: 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. 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. . |
Additional Information |
BISAC Categories: - Technology & Engineering | Remote Sensing & Geographic Information Systems - Technology & Engineering | Environmental - General - Technology & Engineering | Imaging Systems |
Dewey: 621.367 |
LCCN: 2009004453 |
Physical Information: 0.9" H x 6.2" W x 9.3" (1.50 lbs) 376 pages |
Descriptions, Reviews, Etc. |
Publisher Description: 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:
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. |