On Board Processing For Satellite Remote Sensing Images

On-board image processing systems are used to maximize image data transmission efficiency for large volumes of data gathered by Earth observation satellites. This book explains the methods, mathematical models, and key technologies used for these systems. It introduces the background, basic concepts, and the architecture of on-board image processing, along with on-board detection of the image feature and matching, ground control point identification, on-board geometric correction, calibration, geographic registration, etc. • Describes algorithms and methodologies for on-board image processing with FPGA chips. • Migrates the traditional on-ground computing to on-board operation and the image processing is implemented on-board, not on-ground. • Introduces for the first time many key technologies and methods for on-board image processing. • Emphasizes the recent progress in image processing by using on-board FPGA chips. • Includes case studies from the author’s extensive research and experience on the topic. This book gives insights into emerging technologies for on-board processing and will benefit senior undergraduate and graduate students of remote sensing, information technology, computer science and engineering, electronic engineering, and geography, as well as researchers and professionals interested in satellite remote sensing image processing in academia, and governmental and commercial sectors.

"Onboard image processing systems are used to maximize image data transmission efficiency for large volumes of data gathered by Earth observation satellites. This book explains the methods, mathematical models, and key technologies used for these systems. It introduces the background, basic concepts, and the architecture of on-board image processing, along with on-board detection of the image feature and matching, ground control point identification, on-board geometric correction, calibration, geographic registration, etc. A great reference for students, researchers, and professionals in government and industry interested in remote sensing of cities and disaster monitoring"--

The scope of image processing and recognition has broadened due to the gap in scientific visualization. Thus, new imaging techniques have developed, and it is imperative to study this progression for optimal utilization. Big Data Analytics for Satellite Image Processing and Remote Sensing is a critical scholarly resource that examines the challenges and difficulties of implementing big data in image processing for remote sensing and related areas. Featuring coverage on a broad range of topics, such as distributed computing, parallel processing, and spatial data, this book is geared towards scientists, professionals, researchers, and academicians seeking current research on the use of big data analytics in satellite image processing and remote sensing.

This book provides a summary of current research in the application of image registration to satellite imagery. Presenting algorithms for creating mosaics and tracking changes on the planet's surface over time, it is an indispensable resource for researchers and advanced students in Earth and space science, and image processing.

This book is a completely updated, greatly expanded version of the previously successful volume by the author. The Second Edition includes new results and data, and discusses a unified framework and rationale for designing and evaluating image processing algorithms. Written from the viewpoint that image processing supports remote sensing science, this book describes physical models for remote sensing phenomenology and sensors and how they contribute to models for remote-sensing data. The text then presents image processing techniques and interprets them in terms of these models. Spectral, spatial, and geometric models are used to introduce advanced image processing techniques such as hyperspectral image analysis, fusion of multisensor images, and digital elevationmodel extraction from stereo imagery. The material is suited for graduate level engineering, physical and natural science courses, or practicing remote sensing scientists. Each chapter is enhanced by student exercises designed to stimulate an understanding of the material. Over 300 figuresare produced specifically for this book, and numerous tables provide a rich bibliography of the research literature.

On-board image processing systems are used to maximize image data transmission efficiency for large volumes of data gathered by Earth observation satellites. This book explains the methods, mathematical models, and key technologies used for these systems. It introduces the background, basic concepts, and the architecture of on-board image processing, along with on-board detection of the image feature and matching, ground control point identification, on-board geometric correction, calibration, geographic registration, etc. • Describes algorithms and methodologies for on-board image processing with FPGA chips. • Migrates the traditional on-ground computing to on-board operation and the image processing is implemented on-board, not on-ground. • Introduces for the first time many key technologies and methods for on-board image processing. • Emphasizes the recent progress in image processing by using on-board FPGA chips. • Includes case studies from the author’s extensive research and experience on the topic. This book gives insights into emerging technologies for on-board processing and will benefit senior undergraduate and graduate students of remote sensing, information technology, computer science and engineering, electronic engineering, and geography, as well as researchers and professionals interested in satellite remote sensing image processing in academia, and governmental and commercial sectors.

Continuing in the footsteps of the pioneering first edition, Signal and Image Processing for Remote Sensing, Second Edition explores the most up-to-date signal and image processing methods for dealing with remote sensing problems. Although most data from satellites are in image form, signal processing can contribute significantly in extracting information from remotely sensed waveforms or time series data. This book combines both, providing a unique balance between the role of signal processing and image processing. Featuring contributions from worldwide experts, this book continues to emphasize mathematical approaches. Not limited to satellite data, it also considers signals and images from hydroacoustic, seismic, microwave, and other sensors. Chapters cover important topics in signal and image processing and discuss techniques for dealing with remote sensing problems. Each chapter offers an introduction to the topic before delving into research results, making the book accessible to a broad audience. This second edition reflects the considerable advances that have occurred in the field, with 23 of 27 chapters being new or entirely rewritten. Coverage includes new mathematical developments such as compressive sensing, empirical mode decomposition, and sparse representation, as well as new component analysis methods such as non-negative matrix and tensor factorization. The book also presents new experimental results on SAR and hyperspectral image processing. The emphasis is on mathematical techniques that will far outlast the rapidly changing sensor, software, and hardware technologies. Written for industrial and academic researchers and graduate students alike, this book helps readers connect the "dots" in image and signal processing. New in This Edition The second edition includes four chapters from the first edition, plus 23 new or entirely rewritten chapters, and 190 new figures. New topics covered include: Compressive sensing The mixed pixel problem with hyperspectral images Hyperspectral image (HSI) target detection and classification based on sparse representation An ISAR technique for refocusing moving targets in SAR images Empirical mode decomposition for signal processing Feature extraction for classification of remote sensing signals and images Active learning methods in classification of remote sensing images Signal subspace identification of hyperspectral data Wavelet-based multi/hyperspectral image restoration and fusion The second edition is not intended to replace the first edition entirely and readers are encouraged to read both editions of the book for a more complete picture of signal and image processing in remote sensing. See Signal and Image Processing for Remote Sensing (CRC Press 2006).

Edited by leaders in the field, with contributions by a panel of experts, Image Processing for Remote Sensing explores new and unconventional mathematics methods. The coverage includes the physics and mathematical algorithms of SAR images, a comprehensive treatment of MRF-based remote sensing image classification, statistical approaches for