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Multi-resolution Image Fusion in Remote Sensing
Written using clear and accessible language, this useful guide discusses fundamental concepts and practices of multi-resolution image fusion.
Performance and Implementation Aspects of Nonlinear Filtering
Nonlinear filtering is an important standard tool for information and sensor fusion applications, e.g., localization, navigation, and tracking. It is an essential component in surveillance systems and of increasing importance for standard consumer products, such as cellular phones with localization, car navigation systems, and augmented reality. This thesis addresses several issues related to nonlinear filtering, including performance analysis of filtering and detection, algorithm analysis, and various implementation details. The most commonly used measure of filtering performance is the root mean square error (RMSE), which is bounded from below by the Cramér-Rao lower bound (CRLB). This th...
Document and Image Compression
Although it's true that image compression research is a mature field, continued improvements in computing power and image representation tools keep the field spry. Faster processors enable previously intractable compression algorithms and schemes, and certainly the demand for highly portable high-quality images will not abate. Document and Image Compression highlights the current state of the field along with the most probable and promising future research directions for image coding. Organized into three broad sections, the book examines the currently available techniques, future directions, and techniques for specific classes of images. It begins with an introduction to multiresolution ima...
Image Processing for Remote Sensing
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
Hyperspectral Data Compression
Hyperspectral Data Compression provides a survey of recent results in the field of compression of remote sensed 3D data, with a particular interest in hyperspectral imagery. Chapter 1 addresses compression architecture, and reviews and compares compression methods. Chapters 2 through 4 focus on lossless compression (where the decompressed image must be bit for bit identical to the original). Chapter 5, contributed by the editors, describes a lossless algorithm based on vector quantization with extensions to near lossless and possibly lossy compression for efficient browning and pure pixel classification. Chapter 6 deals with near lossless compression while. Chapter 7 considers lossy techniques constrained by almost perfect classification. Chapters 8 through 12 address lossy compression of hyperspectral imagery, where there is a tradeoff between compression achieved and the quality of the decompressed image. Chapter 13 examines artifacts that can arise from lossy compression.
Signal and Image Processing for Remote Sensing
Advances in signal and image processing for remote sensing have been tremendous in recent years. The progress has been particularly significant with the use of deep learning based techniques to solve remote sensing problems. These advancements are the focus of this third edition of Signal and Image Processing for Remote Sensing. It emphasizes the use of machine learning approaches for the extraction of remote sensing information. Other topics include change detection in remote sensing and compressed sensing. With 19 new chapters written by world leaders in the field, this book provides an authoritative examination and offers a unique point of view on signal and image processing. Features Inc...
Medical Image Understanding and Analysis
This book constitutes the refereed proceedings of the 21st Annual Conference on Medical Image Understanding and Analysis, MIUA 2017, held in Edinburgh, UK, in July 2017. The 82 revised full papers presented were carefully reviewed and selected from 105 submissions. The papers are organized in topical sections on retinal imaging, ultrasound imaging, cardiovascular imaging, oncology imaging, mammography image analysis, image enhancement and alignment, modeling and segmentation of preclinical, body and histological imaging, feature detection and classification. The chapters 'Model-Based Correction of Segmentation Errors in Digitised Histological Images' and 'Unsupervised Superpixel-Based Segmentation of Histopathological Images with Consensus Clustering' are open access under a CC BY 4.0 license.
Machine Vision and Advanced Image Processing in Remote Sensing
Since 1994, the European Commission has undertaken various actions to expand the use of Earth observation (EO) from space in the Union and to stimulate value-added services based on the use of Earth observation satellite data.' By supporting research and technological development activities in this area, DG XII responded to the need to increase the cost-effectiveness of space derived environmental information. At the same time, it has contributed to a better exploitation of this unique technology, which is a key source of data for environmental monitoring from local to global scale. MAVIRIC is part of the investment made in the context of the Environ ment and Climate Programme (1994-1998) to...
Satellite Data Compression
Satellite Data Compression covers recent progress in compression techniques for multispectral, hyperspectral and ultra spectral data. A survey of recent advances in the fields of satellite communications, remote sensing and geographical information systems is included. Satellite Data Compression, contributed by leaders in this field, is the first book available on satellite data compression. It covers onboard compression methodology and hardware developments in several space agencies. Case studies are presented on recent advances in satellite data compression techniques via various prediction-based, lookup-table-based, transform-based, clustering-based, and projection-based approaches. This book provides valuable information on state-of-the-art satellite data compression technologies for professionals and students who are interested in this topic. Satellite Data Compression is designed for a professional audience comprised of computer scientists working in satellite communications, sensor system design, remote sensing, data receiving, airborne imaging and geographical information systems (GIS). Advanced-level students and academic researchers will also benefit from this book.
Signal and Image Processing for Remote Sensing, Second Edition
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...
