Earth observation is the field of science concerned with the problem of monitoring and modeling the processes on the Earth surface and their interaction with the atmosphere. The Earth is continuously monitored with advanced optical and radar sensors. The images are analyzed and processed to deliver useful products to individual users, agencies and public administrations. To deal with these problems, remote sensing image processing is nowadays a mature research area, and the techniques developed in the field allow many real-life applications with great societal value. For instance, urban monitoring, fire detection or flood prediction can have a great impact on economical and environmental issues. To attain such objectives, the remote sensing community has turned into a multidisciplinary field of science that embraces physics, signal theory, computer science, electronics and communications. From a machine learning and signal/image processing point of view, all the applications are tackled under specific formalisms, such as classification and clustering, regression and function approximation, data coding, restoration and enhancement, source unmixing, data fusion or feature selection and extraction. This book covers some of the fields in a comprehensive way. Table of Contents: Remote Sensing from Earth Observation Satellites / The Statistics of Remote Sensing Images / Remote Sensing Feature Selection and Extraction / Classification / Spectral Mixture Analysis / Estimation of Physical Parameters

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Gustavo Camps-Valls received a Ph.D. degree in Physics from the Universitat de Valencia, Spain in 2002. He is currently an Associate Professor in the Department of Electronics Engineering and leading researcher at the Image Processing Laboratory (IPL) in the same university. Recently included in the ISI lists as a highly cited researcher, he co-edited the books Kernel methods in bioengineering, signal and image processing (IGI, 2007) and Kernel methods for remote sensing data analysis (Wiley & sons, 2009). Dr. Camps-Valls serves on the Program Committees of International Society for Optical Engineers (SPIE) Europe, International Geoscience and Remote Sensing Symposium (IGARSS), Machine Learning for Signal Processing (MLSP), and International Conference on Image Processing (ICIP). Since 2007 he is member of the Data Fusion technical committee of the IEEE Geoscience and Remote Sensing Society, and since 2009 he is member of the Machine Learning for Signal Processing Technical Committee of the IEEE Signal Processing Society. He is involved in the EUMETSAT MTG-IRS Science Team. He is Associate Editor of the ISRN Signal Processing Journal and IEEE Geoscience and Remote Sensing Letters.Devis Tuia was born in Mendrisio, Switzerland, in 1980. He received a diploma in Geography at the University of Lausanne in 2004, a Master of Advanced Studies in Environmental Engineering at the Federal Institute of Technology of Lausanne (EPFL) in 2005 and a Ph.D. in environmental sciences at the University of Lausanne in 2009. He was a postdoc researcher at both the University of Valencia, Spain and the University of Colorado at Boulder under a Swiss National Foundation program. He is now a research associate at the Laboratoire des Systemes d'Information Geographiques, EPFL. His research interests include the development of algorithms for information extraction and classification of high resolution remote sensing images and socioeconomic data using machine learning algorithms, with particular focus in human-machine interaction and adaptation problems.Luis Gomez-Chova received the B.Sc. (with first-class honors), M.Sc., and Ph.D. degrees in electronics engineering from the University of Valencia, Valencia, Spain, in 2000, 2002 and 2008, respectively. He was awarded by the Spanish Ministry of Education with the National Award for Electronics Engineering. Since 2000, he has been with the Department of Electronics Engineering, University of Valencia, first enjoying a research scholarship from the Spanish Ministry of Education and currently as an Assistant Professor. He is also a researcher at the Image Processing Laboratory (IPL), where his work is mainly related to pattern recognition and machine learning applied to remote sensing multispectral images and cloud screening. He conducts and supervises research on these topics within the frameworks of several national and international projects. He is the author (or coauthor) of 30 international journal papers, more than 90 international conference papers, and several international book chapters.Sandra Jimenez received a MSc in Mathematics and Computer Science in 2010. She's now an Associate Researcher in the Image Processing Laboratory (IPL) at the Universitat de Valencia, where she is currently pursuing her PhD degree on image statistics and their applications in theoretical neuroscience and image analysis.Jesus Malo was born in 1970 and received the M.Sc. degree in Physics in 1995 and the PhD. degree in Physics in 1999, both from the Universitat de Valencia. He was the recipient of the Vistakon European Research Award in 1994. In 2000 and 2001 he worked as Fulbright Postdoc at the Vision Group of the NASA Ames Research Center (with A.B. Watson), and at the Lab of Computational Vision of the Center for Neural Science, New York University (with E.P. Simoncelli). Currently, he is a leading researcher at the Image Processing Laboratory (IPL) at the Universitat de Valencia. He is member of the Asociacion de Mujeres Investigadoras y Tecnologas (AMIT), and Associate Editor of IEEE Transactions on Image Processing. He is interested in models of low-level human vision, their relations with information theory, and their applications to image processing and vision science experimentation. His interests also include (but are not limited to) Fourier, Matlab, Equipo Cronica, Jim Jarmusch, Jordi Savall, Lou Reed, Belle and Sebastian, The Pixies, Milo Manara, la Bola de Cristal, Faemino y Cansado, and beauty in general.

Inhalt
Remote Sensing from Earth Observation Satellites.- The Statistics of Remote Sensing Images.- Remote Sensing Feature Selection and Extraction.- Classification.- Spectral Mixture Analysis.- Estimation of Physical Parameters.