Mode of access: World Wide Web.

System requirements: Adobe Acrobat Reader.

Part of: Synthesis digital library of engineering and computer science.

Series from website.

Includes bibliographical references (p. 123-170) and index.

Preface -- Acknowledgments --

1. Remote sensing from earth observation satellites -- 1.1 Introduction -- 1.1.1 Earth observation, spectroscopy and remote sensing -- 1.1.2 Types of remote sensing instruments -- 1.1.3 Applications of remote sensing -- 1.1.4 The remote sensing system -- 1.2 Fundamentals of optical remote sensing -- 1.2.1 The electromagnetic radiation -- 1.2.2 Solar irradiance -- 1.2.3 Earth atmosphere -- 1.2.4 At-sensor radiance -- 1.3 Multi and hyperspectral sensors -- 1.3.1 Spatial, spectral and temporal resolutions -- 1.3.2 Optical sensors and platforms -- 1.3.3 How do images look like? -- 1.4 Remote sensing pointers -- 1.4.1 Institutions -- 1.4.2 Journals and conferences -- 1.4.3 Remote sensing companies -- 1.4.4 Software packages -- 1.4.5 Data formats and repositories -- 1.5 Summary --

2. The statistics of remote sensing images -- 2.1 Introduction -- 2.2 Second-order spatio-spectral regularities in hyperspectral images -- 2.2.1 Separate spectral and spatial redundancy -- 2.2.2 Joint spatio-spectral smoothness -- 2.3 Application example to coding IASI data -- 2.4 Higher order statistics -- 2.5 Summary --

3. Remote sensing feature selection and extraction -- 3.1 Introduction -- 3.2 Feature selection -- 3.2.1 Filter methods -- 3.2.2 Wrapper methods -- 3.2.3 Feature selection example -- 3.3 Feature extraction -- 3.3.1 Linear methods -- 3.3.2 Nonlinear methods -- 3.3.3 Feature extraction examples -- 3.4 Physically based spectral features -- 3.4.1 Spectral indices -- 3.4.2 Spectral feature extraction examples -- 3.5 Spatial and contextual features -- 3.5.1 Convolution filters -- 3.5.2 Co-occurrence textural features -- 3.5.3 Markov random fields -- 3.5.4 Morphological filters -- 3.5.5 Spatial transforms -- 3.5.6 Spatial feature extraction example -- 3.6 Summary --

4. Classification of remote sensing images -- 4.1 Introduction -- 4.1.1 The classification problem: definitions -- 4.1.2 Datasets considered -- 4.1.3 Measures of accuracy -- 4.2 Land-cover mapping -- 4.2.1 Supervised methods -- 4.2.2 Unsupervised methods -- 4.2.3 A supervised classification example -- 4.3 Change detection -- 4.3.1 Unsupervised change detection -- 4.3.2 Supervised change detection -- 4.3.3 A multiclass change detection example -- 4.4 Detection of anomalies and targets -- 4.4.1 Anomaly detection -- 4.4.2 Target detection -- 4.4.3 A target detection example -- 4.5 New challenges -- 4.5.1 Semisupervised learning -- 4.5.2 A semisupervised learning example -- 4.5.3 Active learning -- 4.5.4 An active learning example -- 4.5.5 Domain adaptation -- 4.6 Summary --

5. Spectral mixture analysis -- 5.1 Introduction -- 5.1.1 Spectral unmixing steps -- 5.1.2 A survey of applications -- 5.1.3 Outline -- 5.2 Mixing models -- 5.2.1 Linear and nonlinear mixing models -- 5.2.2 The linear mixing model -- 5.3 Estimation of the number of end members -- 5.3.1 A comparative analysis of signal subspace algorithms -- 5.4 End member extraction -- 5.4.1 Extraction techniques -- 5.4.2 A note on the variability of end members -- 5.4.3 A comparative analysis of end member extraction algorithms -- 5.5 Algorithms for abundance estimation -- 5.5.1 Linear approaches -- 5.5.2 Nonlinear inversion -- 5.5.3 A comparative analysis of abundance estimation algorithms -- 5.6 Summary --

6. Estimation of physical parameters -- 6.1 Introduction and principles -- 6.1.1 Forward and inverse modeling -- 6.1.2 Undetermination and ill-posed problems -- 6.1.3 Taxonomy of methods and outline -- 6.2 Statistical inversion methods -- 6.2.1 Land inversion models -- 6.2.2 Ocean inversion models -- 6.2.3 Atmosphere inversion models -- 6.3 Physical inversion techniques -- 6.3.1 Optimization inversion methods -- 6.3.2 Genetic algorithms -- 6.3.3 Look-up tables -- 6.3.4 Bayesian methods -- 6.4 Hybrid inversion methods -- 6.4.1 Regression trees -- 6.4.2 Neural networks -- 6.4.3 Kernel methods -- 6.5 Experiments -- 6.5.1 Land surface biophysical parameter estimation -- 6.5.2 Optical oceanic parameter estimation -- 6.5.3 Model inversion of atmospheric sounding data -- 6.6 Summary --

Bibliography -- Author biographies -- Index.

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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.

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Title from PDF t.p. (viewed on January 12, 2012).