Hyperspectral Data Compression and Red Edge Indices
Hyperspectral Data Compression and Red Edge Indices

Author: Abdallah Alomari

Publisher: LAP Lambert Academic Publishing

Published: 2011-09

Total Pages: 88

ISBN-13: 9783845472225

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Lossy hyperspectral image compression techniques are widely used to solve the problems of data size. In this book, we evaluate lossy vector quantization and JPEG2000 hyperspectral data compression algorithms using red edge indices as end-products that we want to retrieve from our data. Two airborne hyperspectral data-sets for vegetated areas were tested, one acquired from (AISA) sensor for area in Tambisan, Malaysia and the other data-set acquired from (Hyspex) sensor for agriculture area in Norway. Two red edge products: Vogelmann1 and NDVI red edge indices were retrieved from each original data cube and from their decompressed data cubes. The standard deviation of percentage difference between a product retrieved from an original data cube and that from its decompressed data cube was used as a measure to quantify the impact of compression on end products. The minimum, maximum and average values of the original and compressed data were also used to quantify the differences in the red edge products.

Hyperspectral Data Compression
Hyperspectral Data Compression

Author: Giovanni Motta

Publisher: Springer Science & Business Media

Published: 2006-06-03

Total Pages: 422

ISBN-13: 0387286004

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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.

Satellite Data Compression
Satellite Data Compression

Author: Bormin Huang

Publisher: Springer Science & Business Media

Published: 2011-11-25

Total Pages: 312

ISBN-13: 1461411831

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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.

Optical Compressive Imaging
Optical Compressive Imaging

Author: Adrian Stern

Publisher: CRC Press

Published: 2016-11-17

Total Pages: 316

ISBN-13: 1315354276

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This dedicated overview of optical compressive imaging addresses implementation aspects of the revolutionary theory of compressive sensing (CS) in the field of optical imaging and sensing. It overviews the technological opportunities and challenges involved in optical design and implementation, from basic theory to optical architectures and systems for compressive imaging in various spectral regimes, spectral and hyperspectral imaging, polarimetric sensing, three-dimensional imaging, super-resolution imaging, lens-free, on-chip microscopy, and phase sensing and retrieval. The reader will gain a complete introduction to theory, experiment, and practical use for reducing hardware, shortening image scanning time, and improving image resolution as well as other performance parameters. Optics practitioners and optical system designers, electrical and optical engineers, mathematicians, and signal processing professionals will all find the book a unique trove of information and practical guidance. Delivers the first book on compressed sensing dealing with system development for a wide variety of optical imaging and sensing applications. Covers the fundamentals of CS theory, including noise and algorithms, as well as basic design approaches for data acquisition in optics. Addresses the challenges of implementing compressed sensing theory in the context of different optical imaging designs, from 3D imaging to tomography and microscopy. Provides an essential resource for the design of new and improved devices with improved image quality and shorter acquisition times. Adrian Stern, PhD, is associate professor and head of the Electro-Optical Engineering Unit at Ben-Gurion University of the Negev, Israel. He is an elected Fellow of SPIE.

Hyperspectral Image Processing
Hyperspectral Image Processing

Author: Liguo Wang

Publisher: Springer

Published: 2015-07-15

Total Pages: 327

ISBN-13: 3662474565

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Based on the authors’ research, this book introduces the main processing techniques in hyperspectral imaging. In this context, SVM-based classification, distance comparison-based endmember extraction, SVM-based spectral unmixing, spatial attraction model-based sub-pixel mapping and MAP/POCS-based super-resolution reconstruction are discussed in depth. Readers will gain a comprehensive understanding of these cutting-edge hyperspectral imaging techniques. Researchers and graduate students in fields such as remote sensing, surveying and mapping, geosciences and information systems will benefit from this valuable resource.

Hyperspectral Image Analysis
Hyperspectral Image Analysis

Author: Saurabh Prasad

Publisher: Springer Nature

Published: 2020-04-27

Total Pages: 464

ISBN-13: 3030386171

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This book reviews the state of the art in algorithmic approaches addressing the practical challenges that arise with hyperspectral image analysis tasks, with a focus on emerging trends in machine learning and image processing/understanding. It presents advances in deep learning, multiple instance learning, sparse representation based learning, low-dimensional manifold models, anomalous change detection, target recognition, sensor fusion and super-resolution for robust multispectral and hyperspectral image understanding. It presents research from leading international experts who have made foundational contributions in these areas. The book covers a diverse array of applications of multispectral/hyperspectral imagery in the context of these algorithms, including remote sensing, face recognition and biomedicine. This book would be particularly beneficial to graduate students and researchers who are taking advanced courses in (or are working in) the areas of image analysis, machine learning and remote sensing with multi-channel optical imagery. Researchers and professionals in academia and industry working in areas such as electrical engineering, civil and environmental engineering, geosciences and biomedical image processing, who work with multi-channel optical data will find this book useful.

Hyperspectral Imaging Technology in Food and Agriculture
Hyperspectral Imaging Technology in Food and Agriculture

Author: Bosoon Park

Publisher: Springer

Published: 2015-09-29

Total Pages: 404

ISBN-13: 1493928368

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Hyperspectral imaging or imaging spectroscopy is a novel technology for acquiring and analysing an image of a real scene by computers and other devices in order to obtain quantitative information for quality evaluation and process control. Image processing and analysis is the core technique in computer vision. With the continuous development in hardware and software for image processing and analysis, the application of hyperspectral imaging has been extended to the safety and quality evaluation of meat and produce. Especially in recent years, hyperspectral imaging has attracted much research and development attention, as a result rapid scientific and technological advances have increasingly taken place in food and agriculture, especially on safety and quality inspection, classification and evaluation of a wide range of food products, illustrating the great advantages of using the technology for objective, rapid, non-destructive and automated safety inspection as well as quality control. Therefore, as the first reference book in the area, Hyperspectral Imaging Technology in Food and Agriculture focuses on these recent advances. The book is divided into three parts, which begins with an outline of the fundamentals of the technology, followed by full covering of the application in the most researched areas of meats, fruits, vegetables, grains and other foods, which mostly covers food safety and quality as well as remote sensing applicable for crop production. Hyperspectral Imaging Technology in Food and Agriculture is written by international peers who have both academic and professional credentials, with each chapter addressing in detail one aspect of the relevant technology, thus highlighting the truly international nature of the work. Therefore the book should provide the engineer and technologist working in research, development, and operations in the food and agricultural industry with critical, comprehensive and readily accessible information on the art and science of hyperspectral imaging technology. It should also serve as an essential reference source to undergraduate and postgraduate students and researchers in universities and research institutions.

Hyperspectral Remote Sensing
Hyperspectral Remote Sensing

Author: Prem Chandra Pandey

Publisher: Elsevier

Published: 2020-08-05

Total Pages: 508

ISBN-13: 0081028954

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Hyperspectral Remote Sensing: Theory and Applications offers the latest information on the techniques, advances and wide-ranging applications of hyperspectral remote sensing, such as forestry, agriculture, water resources, soil and geology, among others. The book also presents hyperspectral data integration with other sources, such as LiDAR, Multi-spectral data, and other remote sensing techniques. Researchers who use this resource will be able to understand and implement the technology and data in their respective fields. As such, it is a valuable reference for researchers and data analysts in remote sensing and Earth Observation fields and those in ecology, agriculture, hydrology and geology. Includes the theory of hyperspectral remote sensing, along with techniques and applications across a variety of disciplines Presents the processing, methods and techniques utilized for hyperspectral remote sensing and in-situ data collection Provides an overview of the state-of-the-art, including algorithms, techniques and case studies