Sar Statistics

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.

Written for students, remote sensing specialists, researchers and SAR system designers, Processing of SAR Data shows how to produce quality SAR images. In particular, this practical reference presents new methods and algorithms concerning the interferometric processing of SAR data with emphasis on system and signal theory, namely how SAR imagery is formed, how interferometry SAR images are created, and a detailed mathematical description of different focussing algorithms. Starting with the processing basics and progressing to the final geo-coded SAR data product, the book describes the complete processing steps in detail. Algorithms based on the effects of side-looking geometry are developed to correct foreshortening, shadowing and layover.

This carefully curated volume presents an in-depth, state-of-the-art discussion on many applications of Synthetic Aperture Radar (SAR). Integrating interdisciplinary sciences, the book features novel ideas, quantitative methods, and research results, promising to advance computational practices and technologies within the academic and industrial communities. SAR applications employ diverse and often complex computational methods rooted in machine learning, estimation, statistical learning, inversion models, and empirical models. Current and emerging applications of SAR data for earth observation, object detection and recognition, change detection, navigation, and interference mitigation are highlighted. Cutting edge methods, with particular emphasis on machine learning, are included. Contemporary deep learning models in object detection and recognition in SAR imagery with corresponding feature extraction and training schemes are considered. State-of-the-art neural network architectures in SAR-aided navigation are compared and discussed further. Advanced empirical and machine learning models in retrieving land and ocean information — wind, wave, soil conditions, among others, are also included.

This book is a collection of the most recent and significant research on algorithms for the analysis of polar sea-ice SAR data. All algorithms are implemented and tested. One chapter is from the Alaskan SAR Facility, the major NASA archive of polar SAR data and a source of many SAR analysis algorithms, including high-level results of such analyses. One chapter has been written jointly by the US and Canadian Ice Centers, which provide e.g., operational sea-ice products to the shipping and oil-drilling industries and to polar explorations. This book will be useful to all researchers in the polar sciences community.

To derive statistics about crime â€" to estimate its levels and trends, assess its costs to and impacts on society, and inform law enforcement approaches to prevent it â€" a conceptual framework for defining and thinking about crime is virtually a prerequisite. Developing and maintaining such a framework is no easy task, because the mechanics of crime are ever evolving and shifting: tied to shifts and development in technology, society, and legislation. Interest in understanding crime surged in the 1920s, which proved to be a pivotal decade for the collection of nationwide crime statistics. Now established as a permanent agency, the Census Bureau commissioned the drafting of a manual for preparing crime statisticsâ€"intended for use by the police, corrections departments, and courts alike. The new manual sought to solve a perennial problem by suggesting a standard taxonomy of crime. Shortly after the Census Bureau issued its manual, the International Association of Chiefs of Police in convention adopted a resolution to create a Committee on Uniform Crime Records â€"to begin the process of describing what a national system of data on crimes known to the police might look like. The key distinction between the rigorous classification proposed in this report and the "classifications" that have come before in U.S. crime statistics is that it is intended to partition the entirety of behaviors that could be considered criminal offenses into mutually exclusive categories. Modernizing Crime Statistics: Report 1: Defining and Classifying Crime assesses and makes recommendations for the development of a modern set of crime measures in the United States and the best means for obtaining them. This first report develops a new classification of crime by weighing various perspectives on how crime should be defined and organized with the needs and demands of the full array of crime data users and stakeholders.

This book is a printed edition of the Special Issue "Advances in SAR: Sensors, Methodologies, and Applications" that was published in Remote Sensing