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Classification Methods for Remotely Sensed Data by Paul Mather,Brandt Tso

Size PDF book: 1984 kb
Size ePub book: 1737 kb
Size Fb2 book: 1696 kb

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.

Reviews (2)
Hi all:
This book is dedicated to researches dealing with digital image processing, mainly satellite digital images. Of course, the methods equally apply to other fields that need to classify a set of objects. The authors' effort is to present alternative methods to classify data based on: artificial neural networks, fuzzy methods, texture quantization, Markov random fields and classification using multi-source.
They present didactically the theoretical aspects of each method and some pseudo codes of some algorithms. The issue is that there is a *BIG* distance between pseudo codes and real codes, in whatever computer languages it may be (C/C++, Java, etc.). This is a weak point in the book. I think it couldn't be hard to the authors present the algorithms in C, for example, since it's not needed define objects, as could be in C++ and Java. And each reader could do the appropriate "translation."
I think that many, many books written to the image processing community could make the work of learning easier if they showed the source code of the algorithms. I can't understand the reason why writers don't do that; may be sadism? Urgh!
I wrote two books on image processing (Portuguese), and my first concern was to present the source code of EACH algorithm and technique used, including the ones presented in this book. I used Java to do that.
So, if you are a writer, please remember the mortals.
...I must say that it is the best book on remote sensing I have ever come across...

ISBN: 1420090720

Rating: 4.9/5

Votes: 280

Other Formats: lrf lrf txt doc

ISBN13: 978-1420090727

Publisher: CRC Press; 2 edition (May 12, 2009)

Language: English

Subcategory: Engineering

Pages: 376

Classification Methods for Remotely Sensed Data
Engineering & Transportation
Author: Paul Mather,Brandt Tso
Title: Classification Methods for Remotely Sensed Data