SAR Image Analysis—A Computational Statistics Approach: With R Code, Data, and Applications [Book Reviews] | IEEE Journals & Magazine | IEEE Xplore
Abstract:
The book SAR Image Analysis—A Computational Statistics Approach: With R Code, Data, and Applications stands out as an exceptional resource dedicated to statistical methodologies to extract information from synthetic aperture radar (SAR) imagery, all within a computational framework using R programming language. The book covers a wide range of topics in 183 pages and seven chapters, including a detailed overview of SAR data acquisition and its strong connection with specific concepts of non-Gaussian statistical models due to the physical properties of the scene. Its primary objective is to consolidate a comprehensive repository about well-established and state-of-the-art parametric models utilized in SAR image processing. It also addresses the critical task of parameter estimation, which is essential for extracting valuable information from the data. Moreover, all of the R codes and datasets are available at www.wiley.com/go/frery/sarimageanalysis, allowing readers to directly apply the concepts discussed and see their practical implications, making the learning experience more engaging as it bridges the gap between theory and practice.
SAR IMAGE ANALYSIS ― A COMPUTATIONAL STATISTICS APPROACH
Authored by Alejandro C. Frery, Jie Wu, and Luiz Gomez Wiley-IEEE Press, 2022, ISBN: 978-1-119-79529-2 (hardcover) 978-1-119-79546-9 (ebook) Amazon
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.
Chapter 1 addresses the acquisition of SAR data from its fundamentals. A sensitive issue in SAR imaging focuses on the complex matter of speckle noise. This granular pattern poses significant challenges, mainly regarding its reduction, as it generally affects the quality of SAR images. It begins by explaining the operation of SAR systems and how they differ from conventional radars, which use electromagnetic signals to illuminate objects and obtain information about their location and physical properties. Key concepts are discussed, such as the polarization of SAR signals and their modes of radiation, which generate different radar operation modes. Furthermore, crucial concepts related to data acquisition, like spatial resolution and SAR imaging techniques, are introduced. The term speckle, inherently related to SAR images, is presented in terms of its physical origin, along with a brief explanation of the radar return signal. The chapter concludes with an interesting overview of SAR systems from major space agencies and the presentation of useful tools for managing freely accessible SAR data.
In Chapter 2, readers will find a discussion encompassing descriptive statistical measures commonly employed in SAR data analysis. It also introduces data visualization tools that provide a preliminary overview of the data distribution, allowing the extraction of valuable insights into the scene. Additionally, the chapter presents image processing operations tailored to leveraging the spatial characteristics of SAR images, such as the histogram equalization method and scattering-based analysis. Each of these elements is presented alongside its corresponding R codes. Some R packages designed for data loading and manipulation are also introduced, with particular attention to the “imagematrix” package. While it is no longer available in the CRAN, the authors have enhanced it with additional features and provided a source code to install it, as well as practical examples to illustrate its usefulness.
Chapter 3 covers we what many authors call fully developed speckle, or speckle for textureless targets.
Within this chapter, the foundational characteristics of SAR data are
thoroughly explored. Under certain assumptions, it can be demonstrated
that the observed signal (intensity SAR data) follows a gamma law.
Furthermore, the concept of the multiplicative model is introduced,
which describes the random variable resulting from the product of the
backscatters and the speckle when considered independent. The chapter
justifies the selection of specific distributions (depending on
distributional assumptions of the backscatterers), such as the
Once the
dataset of interest has been selected and suitable statistical models
have been defined as appropriate stochastic representations for SAR
data, the next step involves parameter estimation. In this context,
Chapter 4 discusses parameter estimation through the analogy method and
maximum likelihood, two fundamental approaches to deal with parametric
inference. The analogy method is more straightforward than maximum
likelihood because it does not require the expression of the density of
the model. In contrast, maximum-likelihood estimators offer several
asymptotic advantages and can be obtained through various optimization
techniques, often starting from an initial analogy estimate. Therefore,
the maximum likelihood is regarded as one of the best options for large
samples when there is no contamination. However, other techniques may be
required to improve the results for small datasets and the chapter
addresses this problem by introducing bootstrap resampling as a
technique to improve the precision of parameter estimates. Finally,
through a series of illustrative examples, Chapter 4 provides a
comparative analysis of these methods using well-known models related to
SAR data, such as the uniform, Gaussian, mixture of Gaussian, gamma,
and
The discussion in Chapter 5 explores practical applications of the theoretical knowledge acquired in earlier chapters. It focuses on implementing statistical models through despeckling filters, including the mean, median, and Lee filters, which are classical methods used for SAR image despeckling. The chapter also explores advanced filters, such as the maximum a posteriori filter and the nonlocal approach. The latter includes the original and the state-of-the-art statistical nonlocal filters employing stochastic distances and hypothesis tests. Additionally, the chapter explores the image classification problem, which consists of assigning a label to a group of pixels based on specific rules. It is addressed through the standard (classical) elemental machine learning methods, which can be categorized into unsupervised and supervised. The latter are more common in SAR data processing due to its superior accuracy and rely only on labeled data. In contrast, unsupervised methods operate autonomously, inferring class information from data. The chapter offers a brief introduction to the most important filter and classification methods, followed by practical applications using both simulated and actual SAR data.
Chapter 6 is dedicated to evaluating the performance and robustness of despeckling filters, which are challenging topics in SAR and statistical modeling. The key concept regarding the first topic is related to the choice of metrics employed. Therefore, the chapter presents a set of well-established image quality indices (metrics) and then discusses some advanced metrics in detail, exploring new proposals that consider not only the typical approach of the filtered image but also the ratio between the observed images and filtered ones (ratio images). Moreover, robustness inference is essential for SAR data analysis that depends on estimation procedures. The objective is to elucidate the notion of statistical robustness within the domain of image processing, offering examples and insights to foster the development of robust inference techniques. Finally, a link to a useful Matlab implementation is provided.
Chapter 7, “Reproducibility and Replicability,” addresses these often-confused terms. It begins by providing clear definitions of reproducibility and replicability, emphasizing their importance in remote sensing and research within the book. It also offers recommendations for good scientific practice. Reproducibility is at the core of experimental sciences, allowing others to replicate results with ease. The concept is exemplified by Isaac Newton’s famous quote, “If they have seen further, it is by standing on the shoulders of Giants.” In contrast, replicability involves transparent reporting, enabling other researchers to perform similar studies. The chapter concludes by underlining the advantages and challenges associated with these concepts within the field of SAR, where rapid technological advancements necessitate efficient algorithms and transparent research practices. It underscores the importance of conducting rigorous, transparent, and ethical research within the remote sensing community.
As conclusive remarks, the book is an exceptional resource that benefits undergraduate and graduate students. It is valuable for statistics students aiming to explore further aspects of SAR image analysis and engineering students seeking a more comprehensive understanding of statistical modeling applied to images. This is achieved by providing a thorough understanding of SAR image analysis throughout its pages, presenting fundamental concepts in an accessible manner while simultaneously delving into specific statistical methodologies. Its broad scope and insightful content accommodate the needs of researchers and students specializing in data analysis and statistics for remote sensing. Whether one’s objective is to acquire a profound theoretical foundation or to apply statistical concepts in practical scenarios, this comprehensive resource offers a wealth of knowledge and guidance. As SAR imagery analysis grows across various domains, this book equips readers with the tools and insights necessary to excel in their research and applications in this field of study.
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