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.

• 1. INTRODUCTION1.1. Hyperspectral Images. From Remote sensing to bench top instruments. A general overview1.2. Hyperspectral cameras. Types of hyperspectral cameras, radiations and sensors2. ALGORITHMS AND METHODS2.1. Pre-processing of hyperspectral images. Spatial and spectral issues2.2. Hyperspectral data compression2.3. Pansharpening2.4. Unsupervised pattern recognition methods2.5. Multivariate Curve Resolution2.6. Non Linear Spectral un-mixing2.7. Variability of the endmembers in spectral unmixing2.8. Regression models2.9. Classical Least Squares for Detection and Classification2.10. Supervised Classification Methods in Hyperspectral Imaging - Recent Advances2.11. Fusion of Hyperspectral Imaging and LiDAR for Forest Monitoring2.12. Hyperspectral time series analysis: Hyperspectral image data streams interpreted by modeling known and unknown variations2.13. Statistical Biophysical Parameter Retrieval and Emulation with Gaussian Processes3. APPLICATION FIELDS3.1. Hyperspectral cameras adapted to the applications. How and when3.2. Applications in Remote Sensing - Natural Landscapes3.3. Applications in Remote Sensing - Anthropogenic activities3.4. Vegetation and crops3.5. Food and feed production3.6. Hyperspectral Imaging for Food related Microbiology Applications3.7. Hyperspectral Imaging in Medical Applications3.8. Hyperspectral Imaging as a part of Pharmaceutical Product Design3.9. Hyperspectral imaging for artworks investigation3.10. Growing applications of hyperspectral and multispectral imaging

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.

• 1. INTRODUCTION1.1. Hyperspectral Images. From Remote sensing to bench top instruments. A general overview1.2. Hyperspectral cameras. Types of hyperspectral cameras, radiations and sensors2. ALGORITHMS AND METHODS2.1. Pre-processing of hyperspectral images. Spatial and spectral issues2.2. Hyperspectral data compression2.3. Pansharpening2.4. Unsupervised pattern recognition methods2.5. Multivariate Curve Resolution2.6. Non Linear Spectral un-mixing2.7. Variability of the endmembers in spectral unmixing2.8. Regression models2.9. Classical Least Squares for Detection and Classification2.10. Supervised Classification Methods in Hyperspectral Imaging - Recent Advances2.11. Fusion of Hyperspectral Imaging and LiDAR for Forest Monitoring2.12. Hyperspectral time series analysis: Hyperspectral image data streams interpreted by modeling known and unknown variations2.13. Statistical Biophysical Parameter Retrieval and Emulation with Gaussian Processes3. APPLICATION FIELDS3.1. Hyperspectral cameras adapted to the applications. How and when3.2. Applications in Remote Sensing - Natural Landscapes3.3. Applications in Remote Sensing - Anthropogenic activities3.4. Vegetation and crops3.5. Food and feed production3.6. Hyperspectral Imaging for Food related Microbiology Applications3.7. Hyperspectral Imaging in Medical Applications3.8. Hyperspectral Imaging as a part of Pharmaceutical Product Design3.9. Hyperspectral imaging for artworks investigation3.10. Growing applications of hyperspectral and multispectral imaging

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.

• 1. INTRODUCTION1.1. Hyperspectral Images. From Remote sensing to bench top instruments. A general overview1.2. Hyperspectral cameras. Types of hyperspectral cameras, radiations and sensors2. ALGORITHMS AND METHODS2.1. Pre-processing of hyperspectral images. Spatial and spectral issues2.2. Hyperspectral data compression2.3. Pansharpening2.4. Unsupervised pattern recognition methods2.5. Multivariate Curve Resolution2.6. Non Linear Spectral un-mixing2.7. Variability of the endmembers in spectral unmixing2.8. Regression models2.9. Classical Least Squares for Detection and Classification2.10. Supervised Classification Methods in Hyperspectral Imaging - Recent Advances2.11. Fusion of Hyperspectral Imaging and LiDAR for Forest Monitoring2.12. Hyperspectral time series analysis: Hyperspectral image data streams interpreted by modeling known and unknown variations2.13. Statistical Biophysical Parameter Retrieval and Emulation with Gaussian Processes3. APPLICATION FIELDS3.1. Hyperspectral cameras adapted to the applications. How and when3.2. Applications in Remote Sensing - Natural Landscapes3.3. Applications in Remote Sensing - Anthropogenic activities3.4. Vegetation and crops3.5. Food and feed production3.6. Hyperspectral Imaging for Food related Microbiology Applications3.7. Hyperspectral Imaging in Medical Applications3.8. Hyperspectral Imaging as a part of Pharmaceutical Product Design3.9. Hyperspectral imaging for artworks investigation3.10. Growing applications of hyperspectral and multispectral imaging