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.
Reihe
Sprache
Verlagsort
Verlagsgruppe
Elsevier Science & Technology
Zielgruppe
Für Beruf und Forschung
Scientists, academics, and graduate students in various disciplines working with hyperspectral images, including remote sensing, vegetation and crops, food and feed production, forensic sciences, biochemistry, medical imaging, pharmaceutical production, and art studies
Illustrationen
Approx. 120 illustrations (40 in full color)
Maße
Höhe: 229 mm
Breite: 152 mm
Gewicht
ISBN-13
978-0-444-63977-6 (9780444639776)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Klassifikation
Jose Manuel Amigo is a Research Professor at IKERBASQUE, the Basque Foundation for Sciences in Bilbao and a Distinguished Professor at the Department of Analytical Chemistry, University of Basque Country, Spain. He obtained his PhD (Cum Laude) in Chemistry from the Autonomous University of Barcelona, Spain. He was employed as a post-doctoral student (2007 - 2009) and an Associate Professor (2010 - 2019) at the Department of Food Science of the University of Copenhagen, Denmark. In 2017, he was at the same time a guest Professor at the Federal University of Pernambuco, Brazil. Current research interests include hyperspectral and digital image analysis and the application of Chemometrics (i.e. Machine and Deep Learning). He has authored over 180 publications (150+ peer-reviewed papers, books, book chapters, proceedings, etc.) and has given more than 60 conferences and courses at international meetings. Jose has supervised or is currently supervising several MSc, PhD and Post Docs, and he is an editorial board member of four scientific journals within chemometrics. Moreover, he received the "2014 Chemometrics and Intelligent Laboratory Systems Award? for his achievements in the field of Chemometrics and the "2019 Tomas Hirschfeld Award? for his achievements in the field of Near Infrared.
Herausgeber*in
Research Professor, IKERBASQUE, Basque Foundation for Sciences. Bilbao and Distinguished Professor at the Department of Analytical Chemistry, University of the Basque Country, Spain
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
