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Book 2016 8th Workshop on Hyperspectral Image and Signal Processing Evolution in Remote Sensing WHISPERS

Download or read book 2016 8th Workshop on Hyperspectral Image and Signal Processing Evolution in Remote Sensing WHISPERS written by IEEE Staff and published by . This book was released on 2016-08-21 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The aim of this workshop is to bring together all the people involved in hyperspectral data processing, generally speaking

Book 2009 First Workshop on Hyperspectral Image and Signal Processing Evolution in Remote Sensing

Download or read book 2009 First Workshop on Hyperspectral Image and Signal Processing Evolution in Remote Sensing written by Institute of Electrical and Electronics Engineers and published by . This book was released on 2009 with total page 636 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Book 2014 6th Workshop on Hyperspectral Image and Signal Processing Evolution in Remote Sensing WHISPERS

Download or read book 2014 6th Workshop on Hyperspectral Image and Signal Processing Evolution in Remote Sensing WHISPERS written by IEEE Staff and published by . This book was released on 2014-06-24 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The aim of this workshop is to bring together all the people involved in hyperspectral data processing, generally speaking

Book Hyperspectral Data Processing

Download or read book Hyperspectral Data Processing written by Chein-I Chang and published by John Wiley & Sons. This book was released on 2013-02-01 with total page 1180 pages. Available in PDF, EPUB and Kindle. Book excerpt: Hyperspectral Data Processing: Algorithm Design and Analysis is a culmination of the research conducted in the Remote Sensing Signal and Image Processing Laboratory (RSSIPL) at the University of Maryland, Baltimore County. Specifically, it treats hyperspectral image processing and hyperspectral signal processing as separate subjects in two different categories. Most materials covered in this book can be used in conjunction with the author’s first book, Hyperspectral Imaging: Techniques for Spectral Detection and Classification, without much overlap. Many results in this book are either new or have not been explored, presented, or published in the public domain. These include various aspects of endmember extraction, unsupervised linear spectral mixture analysis, hyperspectral information compression, hyperspectral signal coding and characterization, as well as applications to conceal target detection, multispectral imaging, and magnetic resonance imaging. Hyperspectral Data Processing contains eight major sections: Part I: provides fundamentals of hyperspectral data processing Part II: offers various algorithm designs for endmember extraction Part III: derives theory for supervised linear spectral mixture analysis Part IV: designs unsupervised methods for hyperspectral image analysis Part V: explores new concepts on hyperspectral information compression Parts VI & VII: develops techniques for hyperspectral signal coding and characterization Part VIII: presents applications in multispectral imaging and magnetic resonance imaging Hyperspectral Data Processing compiles an algorithm compendium with MATLAB codes in an appendix to help readers implement many important algorithms developed in this book and write their own program codes without relying on software packages. Hyperspectral Data Processing is a valuable reference for those who have been involved with hyperspectral imaging and its techniques, as well those who are new to the subject.

Book 2013 5th Workshop on Hyperspectral Image and Signal Processing Evolution in Remote Sensing WHISPERS

Download or read book 2013 5th Workshop on Hyperspectral Image and Signal Processing Evolution in Remote Sensing WHISPERS written by IEEE Staff and published by . This book was released on 2013-06-26 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The aim of this workshop is to bring together all the people involved in hyperspectral data processing, generally speaking By data, we mean signals, as provided by spectrometers and processed individually images, from the ground using microscopes and spectrometers to airborne or satellite sensors, up to astrophysical data models models of the sensors or of the sensed scene, including physical considerations By processing, we mean everything from the acquisition, the calibration to the analysis (image processing, signal processing, feature extraction, dimension reduction, unmixing and source separation, classification)

Book Real Time Progressive Hyperspectral Image Processing

Download or read book Real Time Progressive Hyperspectral Image Processing written by Chein-I Chang and published by Springer. This book was released on 2016-03-22 with total page 629 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book covers the most crucial parts of real-time hyperspectral image processing: causality and real-time capability. Recently, two new concepts of real time hyperspectral image processing, Progressive HyperSpectral Imaging (PHSI) and Recursive HyperSpectral Imaging (RHSI). Both of these can be used to design algorithms and also form an integral part of real time hyperpsectral image processing. This book focuses on progressive nature in algorithms on their real-time and causal processing implementation in two major applications, endmember finding and anomaly detection, both of which are fundamental tasks in hyperspectral imaging but generally not encountered in multispectral imaging. This book is written to particularly address PHSI in real time processing, while a book, Recursive Hyperspectral Sample and Band Processing: Algorithm Architecture and Implementation (Springer 2016) can be considered as its companion book.

Book Hyperspectral Imaging

Download or read book Hyperspectral Imaging written by and published by Elsevier. This book was released on 2019-09-29 with total page 800 pages. Available in PDF, EPUB and Kindle. Book excerpt: Hyperspectral Imaging, Volume 32, presents a comprehensive exploration of the different analytical methodologies applied on hyperspectral imaging and a state-of-the-art analysis of applications in different scientific and industrial areas. This book presents, for the first time, a comprehensive collection of the main multivariate algorithms used for hyperspectral image analysis in different fields of application. The benefits, drawbacks and suitability of each are fully discussed, along with examples of their application. Users will find state-of-the art information on the machinery for hyperspectral image acquisition, along with a critical assessment of the usage of hyperspectral imaging in diverse scientific fields. Provides a comprehensive roadmap of hyperspectral image analysis, with benefits and considerations for each method discussed Covers state-of-the-art applications in different scientific fields Discusses the implementation of hyperspectral devices in different environments

Book Deep Learning for Hyperspectral Image Analysis and Classification

Download or read book Deep Learning for Hyperspectral Image Analysis and Classification written by Linmi Tao and published by Springer Nature. This book was released on 2021-02-20 with total page 207 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on deep learning-based methods for hyperspectral image (HSI) analysis. Unsupervised spectral-spatial adaptive band-noise factor-based formulation is devised for HSI noise detection and band categorization. The method to characterize the bands along with the noise estimation of HSIs will benefit subsequent remote sensing techniques significantly. This book develops on two fronts: On the one hand, it is aimed at domain professionals who want to have an updated overview of how hyperspectral acquisition techniques can combine with deep learning architectures to solve specific tasks in different application fields. On the other hand, the authors want to target the machine learning and computer vision experts by giving them a picture of how deep learning technologies are applied to hyperspectral data from a multidisciplinary perspective. The presence of these two viewpoints and the inclusion of application fields of remote sensing by deep learning are the original contributions of this review, which also highlights some potentialities and critical issues related to the observed development trends.