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. Giulia Gorla is a Postdoctoral Researcher at the Department of Analytical Chemistry, University of Basque Country, Spain. She earned her Bachelor's degree in Chemistry and Industrial Chemistry in 2018 at the University of Insubria, Italy. Subsequently, in 2019, she completed her Master's degree in Chemistry at the same university. In 2023, she achieved her Ph.D. in Chemical and Environmental Sciences, specializing in analytical chemistry, graduating with honors (Cum Laude) with a doctoral thesis titled " Infrared spectroscopy and Chemometrics: facing analytical chemistry issues through data." Since January 2024, she has embarked on her first postdoctoral contract at the Department of Analytical Chemistry at UPV/EHU, related to the HyperSort – Hyperspectral Optical Engine project. Her scientific interests encompass analysing spectroscopic data, hyperspectral imaging, and applying and developing chemometrics, including Machine Learning and Deep Learning techniques. With 14 publications as author, she has supervised 8 undergraduate theses in the Department of Science and High Technology at the University of Insubria and 3 master's theses.

• Section I: Introduction1. Hyperspectral and multispectral imaging: setting the scene2. New hardware achievements3. Types of Hyperspectral images and configuration of the measurementsSection II: Algorithms4. Spectral and Spatial Pre-processing of hyperspectral and multispectral images5. Pansharpening6. Compression (including randomization)7. Unsupervised exploration and clustering of hyperspectral and multispectral images8. Multivariate curve resolution (Spectral Unmixing) for hyperspectral image analysis9. Nonlinear Spectral Unmixing10. Variability of the endmembers in spectral unmixing11. An overview of regression methods in hyperspectral and multispectral imaging12. Target Anomaly Detection methods13. Supervised classification methods in hyperspectral imaging—recent advances14. Fusion of hyperspectral images. A comprehensive perspective15. Fusion of hyperspectral imaging and LiDAR for forest monitoring16. Hyperspectral time series analysis: hyperspectral image data streams interpreted by modelling known and unknown variations17. Statistical biophysical parameter retrieval and emulation with Gaussian processes18. Hyperspectral super-resolution19. Deep Learning in Hyperspectral Imaging20. Spatial and spectral Limits of DetectionSection III: Recent Developments in the Applied Field21. Different applications require different hyperspectral systems and different chemometric methodologies22. Applications in remote sensing: natural landscapes23. Applications in remote sensing: anthropogenic activities24. Hyperspectral imaging in crop fields: precision agriculture25. Food and feed production26. Hyperspectral imaging for food-related microbiology applications27. Hyperspectral imaging in medical applications28. Hyperspectral imaging as a part of pharmaceutical product design29. Hyperspectral imaging for artwork investigation30. Industrial Hyperspectral Applications31. Plastics in the environment32. Forensic sciences33. Planetary Science and Hyperspectral Imaging34. Growing applications of hyperspectral and multispectral imagingSection IV: Programming35. A brief introduction to available hyperspectral image datasets and software that have been used in this book36. Programming in Matlab37. Programming in R38. Programming in Python

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. Giulia Gorla is a Postdoctoral Researcher at the Department of Analytical Chemistry, University of Basque Country, Spain. She earned her Bachelor's degree in Chemistry and Industrial Chemistry in 2018 at the University of Insubria, Italy. Subsequently, in 2019, she completed her Master's degree in Chemistry at the same university. In 2023, she achieved her Ph.D. in Chemical and Environmental Sciences, specializing in analytical chemistry, graduating with honors (Cum Laude) with a doctoral thesis titled " Infrared spectroscopy and Chemometrics: facing analytical chemistry issues through data." Since January 2024, she has embarked on her first postdoctoral contract at the Department of Analytical Chemistry at UPV/EHU, related to the HyperSort – Hyperspectral Optical Engine project. Her scientific interests encompass analysing spectroscopic data, hyperspectral imaging, and applying and developing chemometrics, including Machine Learning and Deep Learning techniques. With 14 publications as author, she has supervised 8 undergraduate theses in the Department of Science and High Technology at the University of Insubria and 3 master's theses.

• Section I: Introduction1. Hyperspectral and multispectral imaging: setting the scene2. New hardware achievements3. Types of Hyperspectral images and configuration of the measurementsSection II: Algorithms4. Spectral and Spatial Pre-processing of hyperspectral and multispectral images5. Pansharpening6. Compression (including randomization)7. Unsupervised exploration and clustering of hyperspectral and multispectral images8. Multivariate curve resolution (Spectral Unmixing) for hyperspectral image analysis9. Nonlinear Spectral Unmixing10. Variability of the endmembers in spectral unmixing11. An overview of regression methods in hyperspectral and multispectral imaging12. Target Anomaly Detection methods13. Supervised classification methods in hyperspectral imaging—recent advances14. Fusion of hyperspectral images. A comprehensive perspective15. Fusion of hyperspectral imaging and LiDAR for forest monitoring16. Hyperspectral time series analysis: hyperspectral image data streams interpreted by modelling known and unknown variations17. Statistical biophysical parameter retrieval and emulation with Gaussian processes18. Hyperspectral super-resolution19. Deep Learning in Hyperspectral Imaging20. Spatial and spectral Limits of DetectionSection III: Recent Developments in the Applied Field21. Different applications require different hyperspectral systems and different chemometric methodologies22. Applications in remote sensing: natural landscapes23. Applications in remote sensing: anthropogenic activities24. Hyperspectral imaging in crop fields: precision agriculture25. Food and feed production26. Hyperspectral imaging for food-related microbiology applications27. Hyperspectral imaging in medical applications28. Hyperspectral imaging as a part of pharmaceutical product design29. Hyperspectral imaging for artwork investigation30. Industrial Hyperspectral Applications31. Plastics in the environment32. Forensic sciences33. Planetary Science and Hyperspectral Imaging34. Growing applications of hyperspectral and multispectral imagingSection IV: Programming35. A brief introduction to available hyperspectral image datasets and software that have been used in this book36. Programming in Matlab37. Programming in R38. Programming in Python

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. Giulia Gorla is a Postdoctoral Researcher at the Department of Analytical Chemistry, University of Basque Country, Spain. She earned her Bachelor's degree in Chemistry and Industrial Chemistry in 2018 at the University of Insubria, Italy. Subsequently, in 2019, she completed her Master's degree in Chemistry at the same university. In 2023, she achieved her Ph.D. in Chemical and Environmental Sciences, specializing in analytical chemistry, graduating with honors (Cum Laude) with a doctoral thesis titled " Infrared spectroscopy and Chemometrics: facing analytical chemistry issues through data." Since January 2024, she has embarked on her first postdoctoral contract at the Department of Analytical Chemistry at UPV/EHU, related to the HyperSort – Hyperspectral Optical Engine project. Her scientific interests encompass analysing spectroscopic data, hyperspectral imaging, and applying and developing chemometrics, including Machine Learning and Deep Learning techniques. With 14 publications as author, she has supervised 8 undergraduate theses in the Department of Science and High Technology at the University of Insubria and 3 master's theses.

• Section I: Introduction1. Hyperspectral and multispectral imaging: setting the scene2. New hardware achievements3. Types of Hyperspectral images and configuration of the measurementsSection II: Algorithms4. Spectral and Spatial Pre-processing of hyperspectral and multispectral images5. Pansharpening6. Compression (including randomization)7. Unsupervised exploration and clustering of hyperspectral and multispectral images8. Multivariate curve resolution (Spectral Unmixing) for hyperspectral image analysis9. Nonlinear Spectral Unmixing10. Variability of the endmembers in spectral unmixing11. An overview of regression methods in hyperspectral and multispectral imaging12. Target Anomaly Detection methods13. Supervised classification methods in hyperspectral imaging—recent advances14. Fusion of hyperspectral images. A comprehensive perspective15. Fusion of hyperspectral imaging and LiDAR for forest monitoring16. Hyperspectral time series analysis: hyperspectral image data streams interpreted by modelling known and unknown variations17. Statistical biophysical parameter retrieval and emulation with Gaussian processes18. Hyperspectral super-resolution19. Deep Learning in Hyperspectral Imaging20. Spatial and spectral Limits of DetectionSection III: Recent Developments in the Applied Field21. Different applications require different hyperspectral systems and different chemometric methodologies22. Applications in remote sensing: natural landscapes23. Applications in remote sensing: anthropogenic activities24. Hyperspectral imaging in crop fields: precision agriculture25. Food and feed production26. Hyperspectral imaging for food-related microbiology applications27. Hyperspectral imaging in medical applications28. Hyperspectral imaging as a part of pharmaceutical product design29. Hyperspectral imaging for artwork investigation30. Industrial Hyperspectral Applications31. Plastics in the environment32. Forensic sciences33. Planetary Science and Hyperspectral Imaging34. Growing applications of hyperspectral and multispectral imagingSection IV: Programming35. A brief introduction to available hyperspectral image datasets and software that have been used in this book36. Programming in Matlab37. Programming in R38. Programming in Python