Algorithms For Multispectral And Hyperspectral Imagery Iii
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Algorithms for Multispectral and Hyperspectral Imagery III
Author | : A. Evan Iverson |
Publisher | : SPIE-International Society for Optical Engineering |
Total Pages | : 260 |
Release | : 1997 |
Genre | : Computer algorithms |
ISBN | : 9780819424860 |
Hyperspectral Data Processing
Author | : Chein-I Chang |
Publisher | : John Wiley & Sons |
Total Pages | : 1180 |
Release | : 2013-04-08 |
Genre | : Technology & Engineering |
ISBN | : 0471690562 |
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.
Algorithms for Multispectral and Hyperspectral Imagery II
Author | : A. Evan Iverson |
Publisher | : |
Total Pages | : 402 |
Release | : 1996 |
Genre | : Computer algorithms |
ISBN | : 9780819421395 |
Algorithms for Multispectral, Hyperspectral, and Ultraspectral Imagery VI
Author | : Michael R. Descour |
Publisher | : Society of Photo Optical |
Total Pages | : 534 |
Release | : 2000 |
Genre | : Technology & Engineering |
ISBN | : 9780819436757 |
Later ed. of: Algorithms for multispectral and hyperspectral imagery.
Algorithms for Multispectral and Hyperspectral Imagery V
Author | : Society of Photo-optical Instrumentation Engineers |
Publisher | : Society of Photo Optical |
Total Pages | : 206 |
Release | : 1999 |
Genre | : Science |
ISBN | : 9780819431912 |
Algorithms for Multispectral and Hyperspectral Imagery
Author | : A. Evan Iverson |
Publisher | : |
Total Pages | : 200 |
Release | : 1994 |
Genre | : Computer algorithms |
ISBN | : |
Hyperspectral Image Analysis
Author | : Saurabh Prasad |
Publisher | : Springer Nature |
Total Pages | : 464 |
Release | : 2020-04-27 |
Genre | : Computers |
ISBN | : 3030386171 |
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.