This text aims to offer information on research approaches to assessing global environment changes. It includes suggestions for the exchange of ideas between those studying land surface and remote sensing specialists, and advocates synthesizing the findings of different disciplines.

This book addresses opportunities for observation of earth surface processes using remote sensing from space. It should provide a data source for scientists studying global change. The challenges created by global environmental change demand research approaches that are worldwide in scope and interdisciplinary in application. This book is a response to the need for the interchange of ideas and experiences between researchers from those sciences studying the land surface of the earth, its form, and features, and remote sensing specialists. It is founded on the premise that attention should focus on the analysis of specific components of the earth system and on the synthesis of the findings of individual disciplines in terms of how the earth and its atmosphere function as an open system. Earth system science aims to identify how this system changes (both over human and geological timescales) and how such changes can be predicted. Reliable prediction requires scientific understanding, which in turn requires models, theories and data. Remote sensing is capable of providing such data on appropriate temporal and spatial scales.

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.