UID:
almafu_9960832136302883
Format:
1 online resource (xxv, 182 pages) :
,
illustrations, maps
ISBN:
9781119795469
,
111979546X
,
9781119795520
,
1119795524
,
9781119795322
,
111979532X
Content:
"SAR Image Analysis - A Computational Statistics Approach describes, in a practical manner, how to use statistics to extract information from SAR imagery. It covers models, supplies data and code, and discusses theoretical aspects that are relevant to practitioners. This book expertly provides theoretical properties of adequate models, estimators, interpretation, data visualization, and advanced techniques, along with data and code snippets, suitable for students to learn effectively and practically. Comprised of 8 chapters, SAR Image Analyses offers readers a wealth of information, enabling them to use this information in Data Analysis and Statistics courses. This book enables readers to learn not just through theory, but through plenty of exercises demonstrates throughout. With R being the computational platform of choice, this text will be used by a large population of the statistical community, reaching wider audiences than your average day-to-day textbook."--
Note:
Foreword -- Preface -- Acknowledgments -- Acronyms -- Introduction -- I.1 SAR -- I.2 Statistics for SAR -- I.3 The Book -- I.4 Commitment to Reproducibility and Replicability -- 1 Data Acquisition -- 1.1 Introduction -- 1.2 SAR -- 1.2.1 The radar -- 1.2.2 What is SAR? -- 1.2.3 SAR systems -- 1.2.4 The synthetic antenna -- 1.3 Spatial resolution -- 1.4 SAR Imaging Techniques -- 1.5 The Return Signal: backscatter and speckle -- 1.5.1 Backscatter -- 1.5.2 Speckle -- 1.5.3 SAR geometric distortions -- 1.6 SAR Satellites -- 1.7 Preprocessing SAR data -- 1.8 Copernicus Open Access Hub -- 1.9 NASA Earth Data Open Data -- 1.10 Actual SAR Data Examples -- 1.10.1 Hawaii’s Big Island -- 1.10.2 Other examples -- Exercises -- 2 Elements of Data Analysis and Image Processing with R -- 2.1 Useful R Packages -- 2.1.1 Data loading -- 2.1.2 Data manipulation -- 2.2 Descriptive Statistics -- 2.2.1 Center tendency of data -- 2.2.2 Dispersion of data -- 2.2.3 Shape of data -- 2.3 Visualization -- 2.3.1 Rug and box plots -- 2.3.2 Histogram -- 2.3.3 Scattering Diagram -- 2.4 Statistics and Image Processing -- 2.4.1 Histogram based Image Transformation -- 2.4.2 Scattering based Analysis -- 2.5 The imagematrix package -- 3 Intensity SAR Data and the Multiplicative Model -- 3.1 The K distribution -- 3.2 The G0 distribution -- 3.3 The GH distribution -- 3.4 Connection between Models -- Exercises -- 4 Parameter Estimation -- 4.1 Models -- 4.1.1 The Bernoulli distribution -- 4.1.2 The Binomial distribution -- 4.1.3 The Negative Binomial distribution -- 4.1.4 The Uniform distribution -- 4.1.5 Beta distribution -- 4.1.6 The Gaussian distribution -- 4.1.7 Mixture of Gaussian distributions -- 4.1.8 The (SAR) Gamma distribution -- 4.1.9 The Reciprocal Gamma distribution -- 4.1.10 The G0I distribution -- 4.2 Inference by analogy -- 4.2.1 The Uniform distribution -- 4.2.2 The Gaussian distribution -- 4.2.3 Mixture of Gaussian distributions -- 4.2.4 The (SAR) Gamma distribution -- 4.3 Inference by maximum likelihood -- 4.3.1 The Uniform distribution -- 4.3.2 The Gaussian distribution -- 4.3.3 Mixture of Gaussian distributions -- 4.3.4 The (SAR) Gamma distribution -- 4.3.5 The G0 distribution -- 4.4 Analogy vs. Maximum Likelihood -- 4.5 Improvement by bootstrap -- 4.6 Comparison of estimators -- 4.7 An example -- 4.8 The same example, revisited -- 4.9 Another example -- Exercises -- 5 Applications -- 5.1 Statistical filters: Mean, Median, Lee -- 5.1.1 Mean filter -- 5.1.2 Median filter -- 5.1.3 Lee filter -- 5.2 Advanced filters: MAP and Nonlocal Means -- 5.2.1 MAP Filters -- 5.2.2 Nonlocal Means Filter -- 5.2.3 Statistical NLM filters -- 5.2.4 The statistical test -- 5.3 Implementation Details -- 5.4 Results -- 5.5 Classification -- 5.5.1 The image space of the SAR data -- 5.5.2 The feature space -- 5.5.3 Similarity criterion -- 5.6 Supervised Image Classification of SAR Data -- 5.6.1 The nearest neighbor classifier -- 5.6.2 The K-nn method -- 5.7 Maximum Likelihood Classifier -- 5.8 Unsupervised Image Classification of SAR Data: The K-means classifier -- 5.9 Assessment of Classification Results -- Exercises -- 6 Advanced Topics -- 6.1 Assessment of Despeckling Filters -- 6.2 Standard Metrics -- 6.2.1 Advanced Metrics for SAR Despeckling Assessment -- 6.2.2 Completing the Assessment -- 6.3 Robustness -- 6.3.1 Robust inference -- 6.3.2 The mean and the median -- 6.3.3 Empirical Stylized Influence Function -- 6.4 Rejoinder and Recommendations -- 7 Reproducibility and Replicability -- 7.1 What Is Reproducibility? -- 7.2 What Is Replicability? -- 7.3 Reproducibility and Replicability: Benefits for the Remote Sensing Community -- 7.4 Recommendations for making "good science" -- 7.5 Conclusions -- Index.
Additional Edition:
Print version: Frery, Alejandro C. SAR image analysis Hoboken, NJ : Wiley-IEEE Press, 2022 ISBN 9781119795292
Language:
English
Keywords:
Electronic books.
;
Electronic books.
;
Electronic books.
DOI:
10.1002/9781119795520
URL:
https://onlinelibrary.wiley.com/doi/book/10.1002/9781119795520
URL:
https://onlinelibrary.wiley.com/doi/book/10.1002/9781119795520
URL:
https://onlinelibrary.wiley.com/doi/book/10.1002/9781119795520
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