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…mehr
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.
Dr. José Manuel Amigo obtained his PhD (Cum Laude) in Chemistry from the Autonomous University of Barcelona, Spain. Since 2007 he has been employed at the Department of Food Science of the University of Copenhagen, Denmark, as an associate professor. During 2017 he was a guest professor of the Federal University of Pernambuco, Brazil. Current research interests include hyperspectral and digital image analysis, food sciences, environmental modelling, curve resolution, and teaching chemometrics. He has authored more than 160 publications (115+ peer-reviewed papers, books, book chapters, proceedings, etc.) and given more than 60 conferences and courses at international meetings. Jose has supervised or is currently supervising several Masters, post-docs, and PhD students, and he is an editorial board member of four scientific journals (including Chemometrics and Intelligent Laboratory Systems and Analytica Chimica Acta). He received the "2014 Chemometrics and Intelligent Laboratory Sy
stems Award? for his achievements in the field of Chemometrics.
Inhaltsangabe
1. INTRODUCTION 1.1. Hyperspectral Images. From Remote sensing to bench top instruments. A general overview 1.2. Hyperspectral cameras. Types of hyperspectral cameras, radiations and sensors
2. ALGORITHMS AND METHODS 2.1. Pre-processing of hyperspectral images. Spatial and spectral issues 2.2. Hyperspectral data compression 2.3. Pansharpening 2.4. Unsupervised pattern recognition methods 2.5. Multivariate Curve Resolution 2.6. Non Linear Spectral un-mixing 2.7. Variability of the endmembers in spectral unmixing 2.8. Regression models 2.9. Classical Least Squares for Detection and Classification 2.10. Supervised Classification Methods in Hyperspectral Imaging - Recent Advances 2.11. Fusion of Hyperspectral Imaging and LiDAR for Forest Monitoring 2.12. Hyperspectral time series analysis: Hyperspectral image data streams interpreted by modeling known and unknown variations 2.13. Statistical Biophysical Parameter Retrieval and Emulation with Gaussian Processes
3. APPLICATION FIELDS 3.1. Hyperspectral cameras adapted to the applications. How and when 3.2. Applications in Remote Sensing - Natural Landscapes 3.3. Applications in Remote Sensing - Anthropogenic activities 3.4. Vegetation and crops 3.5. Food and feed production 3.6. Hyperspectral Imaging for Food related Microbiology Applications 3.7. Hyperspectral Imaging in Medical Applications 3.8. Hyperspectral Imaging as a part of Pharmaceutical Product Design 3.9. Hyperspectral imaging for artworks investigation 3.10. Growing applications of hyperspectral and multispectral imaging
1. INTRODUCTION 1.1. Hyperspectral Images. From Remote sensing to bench top instruments. A general overview 1.2. Hyperspectral cameras. Types of hyperspectral cameras, radiations and sensors
2. ALGORITHMS AND METHODS 2.1. Pre-processing of hyperspectral images. Spatial and spectral issues 2.2. Hyperspectral data compression 2.3. Pansharpening 2.4. Unsupervised pattern recognition methods 2.5. Multivariate Curve Resolution 2.6. Non Linear Spectral un-mixing 2.7. Variability of the endmembers in spectral unmixing 2.8. Regression models 2.9. Classical Least Squares for Detection and Classification 2.10. Supervised Classification Methods in Hyperspectral Imaging - Recent Advances 2.11. Fusion of Hyperspectral Imaging and LiDAR for Forest Monitoring 2.12. Hyperspectral time series analysis: Hyperspectral image data streams interpreted by modeling known and unknown variations 2.13. Statistical Biophysical Parameter Retrieval and Emulation with Gaussian Processes
3. APPLICATION FIELDS 3.1. Hyperspectral cameras adapted to the applications. How and when 3.2. Applications in Remote Sensing - Natural Landscapes 3.3. Applications in Remote Sensing - Anthropogenic activities 3.4. Vegetation and crops 3.5. Food and feed production 3.6. Hyperspectral Imaging for Food related Microbiology Applications 3.7. Hyperspectral Imaging in Medical Applications 3.8. Hyperspectral Imaging as a part of Pharmaceutical Product Design 3.9. Hyperspectral imaging for artworks investigation 3.10. Growing applications of hyperspectral and multispectral imaging
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