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:

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.

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:

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.

Image processing is concerned with the analysis and manipulation of images by computer. Providing a thorough treatment of image processing with an emphasis on those aspects most used in computer graphics, the authors concentrate on describing and analyzing the underlying concepts rather than on presenting algorithms or pseudocode. As befits a modern introduction to this topic, a good balance is struck between discussing the underlying mathematics and the main topics: signal processing, data discretization, the theory of colour and different colour systems, operations in images, dithering and half-toning, warping and morphing and image processing.

This second edition reflects recent trends in science andtechnology that exploit image processing in computer graphics and vision applications. Stochastic image models and statistical methods for image processing are covered as are: A modern approach and new developments in the area, Probability theory for image processing, Applications in image analysis and computer vision.

This book presents high-quality research papers presented at the International Conference on Computational Intelligence and Data Analytics (ICCIDA 2024), organized by the Department of Information Technology, Vasavi College of Engineering, Hyderabad, India, in June 2024. ICCIDA provides an excellent platform for exchanging knowledge with the global community of scientists, engineers, and educators. This book covers cutting-edge research in two prominent areas—computational intelligence and data analytics and allied research areas.

The book explores the convergence of healthcare and cutting-edge technology, making it a captivating subject for readers interested in future research. Smart healthcare with machine learning techniques offers a transformative paradigm that utilizes the power of new technology, data analytics, and interconnected devices to enhance the quality, efficiency, and accessibility of healthcare services. This involves leveraging Internet of Things (IoT) devices, wearable technology, and machine learning algorithms to monitor patient health, predict medical conditions, and offer personalized treatment recommendations. This innovative combination not only enhances diagnostics and treatment but also addresses the research challenges of healthcare access and delivery in an increasingly connected world. By exploring the synergy between smart healthcare and machine learning, the book helps to understand how these technologies can collaborate to revolutionize patient care and healthcare delivery. This book is an outcome with applications of future technologies to overcome the toughest humanitarian challenges from an engineering 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:

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.

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:

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.

Image processing is concerned with the analysis and manipulation of images by computer. Providing a thorough treatment of image processing with an emphasis on those aspects most used in computer graphics, the authors concentrate on describing and analyzing the underlying concepts rather than on presenting algorithms or pseudocode. As befits a modern introduction to this topic, a good balance is struck between discussing the underlying mathematics and the main topics: signal processing, data discretization, the theory of colour and different colour systems, operations in images, dithering and half-toning, warping and morphing and image processing.

This second edition reflects recent trends in science andtechnology that exploit image processing in computer graphics and vision applications. Stochastic image models and statistical methods for image processing are covered as are: A modern approach and new developments in the area, Probability theory for image processing, Applications in image analysis and computer vision.

This book presents high-quality research papers presented at the International Conference on Computational Intelligence and Data Analytics (ICCIDA 2024), organized by the Department of Information Technology, Vasavi College of Engineering, Hyderabad, India, in June 2024. ICCIDA provides an excellent platform for exchanging knowledge with the global community of scientists, engineers, and educators. This book covers cutting-edge research in two prominent areas—computational intelligence and data analytics and allied research areas.

The book explores the convergence of healthcare and cutting-edge technology, making it a captivating subject for readers interested in future research. Smart healthcare with machine learning techniques offers a transformative paradigm that utilizes the power of new technology, data analytics, and interconnected devices to enhance the quality, efficiency, and accessibility of healthcare services. This involves leveraging Internet of Things (IoT) devices, wearable technology, and machine learning algorithms to monitor patient health, predict medical conditions, and offer personalized treatment recommendations. This innovative combination not only enhances diagnostics and treatment but also addresses the research challenges of healthcare access and delivery in an increasingly connected world. By exploring the synergy between smart healthcare and machine learning, the book helps to understand how these technologies can collaborate to revolutionize patient care and healthcare delivery. This book is an outcome with applications of future technologies to overcome the toughest humanitarian challenges from an engineering approach.