Jie Wu

Luis Gomez

Alejandro C. Frery

SAR IMAGE ANALYSIS — A COMPUTATIONAL STATISTICS APPROACH Discover how to use statistics to extract information from SAR imagery In SAR Image Analysis — A Computational Statistics Approach, an accomplished team of researchers delivers a practical exploration of how to use statistics to extract information from SAR imagery. The authors discuss various models, supply sample data and code, and explain theoretical aspects of SAR image analysis that are highly relevant to practitioners and students. The book offers the theoretical properties of models, estimators, interpretation, data visualization, and advanced techniques, along with the data and code samples, that students require to learn effectively and efficiently. SAR Image Analysis — A Computational Statistics Approach provides various exercises throughout the book to help readers reinforce and retain the extensive information on parameter estimation, applications, reproducibility, replicability, and advanced topics, like robust estimators and stochastic distances, contained within. The book also includes: Thorough introductions to data acquisition and the elements of data analysis and image processing with R, including useful R packages, preprocessing SAR data, and visualization Comprehensive explorations of intensity SAR data and the multiplicative model, including the (SAR) gamma distribution, the K distribution, the G0 distribution, and more general distributions under the multiplicative model Practical discussions of parameter estimations, including the Bernoulli distribution, the negative binomial distribution, and the uniform distribution In-depth examinations of applications, including statistical filters and classification Perfect for undergraduate and graduate students studying remote sensing, data analysis, and statistics, SAR Image Analysis — A Computational Statistics Approach is also an indispensable resource for researchers, practitioners, and professionals seeking a one-stop resource on how to use statistics to extract information from SAR imagery.

With R Code, Data, and Applications

SAR Image Analysis - A Computational Statistics Approach

Luis Alvarez

Marta Mejail

Julio Jacobo

This book constitutes the refereed proceedings of the 17th Iberoamerican Congress on Pattern Recognition, CIARP 2012, held in Buenos Aires, Argentina, in September 2012. The 109 papers presented, among them two tutorials and four keynotes, were carefully reviewed and selected from various submissions. The papers are organized in topical sections on face and iris: detection and recognition; clustering; fuzzy methods; human actions and gestures; graphs; image processing and analysis; shape and texture; learning, mining and neural networks; medical images; robotics, stereo vision and real time; remote sensing; signal processing; speech and handwriting analysis; statistical pattern recognition; theoretical pattern recognition; and video analysis.

17th Iberoamerican Congress, CIARP 2012, Buenos Aires, Argentina, September 3-6, 2012, Proceedings

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

Alejandro Frery

Discover how to use statistics to extract information from SAR imagery In SAR Image Analysis – A Computational Statistics Approach, an accomplished team of researchers delivers a practical exploration of how to use statistics to extract information from SAR imagery. The authors discuss various models, supply sample data and code, and explain theoretical aspects of SAR image analysis that are highly relevant to practitioners and students. The book offers the theoretical properties of models, estimators, interpretation, data visualization, and advanced techniques, along with the data and code samples, that students require to learn effectively and efficiently. SAR Image Analysis – A Computational Statistics Approach provides various exercises throughout the book to help readers reinforce and retain the extensive information on parameter estimation, applications, reproducibility, replicability, and advanced topics, like robust estimators and stochastic distances, contained within. The book also includes: Thorough introductions to data acquisition and the elements of data analysis and image processing with R, including useful R packages, preprocessing SAR data, and visualization Comprehensive explorations of intensity SAR data and the multiplicative model, including the (SAR) gamma distribution, the K distribution, the G0 distribution, and more general distributions under the multiplicative model Practical discussions of parameter estimations, including the Bernoulli distribution, the negative binomial distribution, and the uniform distribution In-depth examinations of applications, including statistical filters and classification Perfect for undergraduate and graduate students studying remote sensing, data analysis, and statistics, SAR Image Analysis – A Computational Statistics Approach is also an indispensable resource for researchers, practitioners, and professionals seeking a one-stop resource on how to use statistics to extract information from SAR imagery.

Luis Gomez

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