SAR Image Analysis - A Computational Statistics Approach: With R Code, Data, and Applications - Tapa dura

Acerca del autor

Alejandro C. Frery, PhD, is Professor of Statistics and Data Science at the School of Mathematics and Statistics at Victoria University at Wellington, New Zealand. He earned his doctorate in Applied Computing at the National Institute for Space Research in Brazil.

Jie Wu, PhD, is Associate Professor at the School of Computer Science, Shaanxi Normal University, China. He received his doctorate in Computer Science and Technology from Xidian University in China.

Luis Gomez, PhD, is Associate Professor at the School of Telecommunications and Electronics Engineering, University of Las Palmas de Gran Canaria, Spain. He received his doctorate in Telecommunication Engineering from the Universidad de Las Palmas de Gran Canaria.

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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.