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.
1. Introduction - p. xcvii 2. Chapter 1.1. Hyperspectral and multispectral imaging: setting the scene - p. 3 3. Chapter 1.2. Configuration of hyperspectral and multispectral imaging systems - p. 17 4. Algorithms and methods - p. 34 5. Chapter 2.1. Preprocessing of hyperspectral and multispectral images - p. 37 6. Chapter 2.2. Hyperspectral compression - p. 55 7. Chapter 2.3. Pansharpening - p. 69 8. Chapter 2.4. Unsupervised exploration of hyperspectral and multispectral images - p. 93 9. Chapter 2.5. Multivariate curve resolution for hyperspectral image analysis - p. 115 10. Chapter 2.6. Nonlinear spectral unmixing - p. 151 11. Chapter 2.7. Variability of the endmembers in spectral unmixing - p. 167 12. Chapter 2.8. An overview of regression methods in hyperspectral and multispectral imaging - p. 205 13. Chapter 2.9. Classical least squares for detection and classification - p. 231 14. Chapter 2.10. Supervised classification methods in hyperspectral imaging—recent advances - p. 247 15. Chapter 2.11. Fusion of hyperspectral imaging and LiDAR for forest monitoring - p. 281 16. Chapter 2.12. Hyperspectral time series analysis: hyperspectral image data streams interpreted by modeling known and unknown variations - p. 305 17. Chapter 2.13. Statistical biophysical parameter retrieval and emulation with Gaussian processes - p. 333 18. Application fields - p. 368 19. Chapter 3.1. Applications in remote sensing—natural landscapes - p. 371 20. Chapter 3.2. Applications in remote sensing—anthropogenic activities - p. 411 21. Chapter 3.3. Hyperspectral imaging in crop fields: precision agriculture - p. 453 22. Chapter 3.4. Food and feed production - p. 475 23. Chapter 3.5. Hyperspectral imaging for food-related microbiology applications - p. 493 24. Chapter 3.6. Hyperspectral imaging in medical applications - p. 523 25. Chapter 3.7. Hyperspectral imaging as a part of pharmaceutical product design - p. 567 26. Chapter 3.8. Hyperspectral imaging for artworks investigation - p. 583 27. Chapter 3.9. Growing applications of hyperspectral and multispectral imaging - p. 605 28. Author Index - p. 631 29. Subject Index - p. 675
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.
1. Introduction - p. xcvii 2. Chapter 1.1. Hyperspectral and multispectral imaging: setting the scene - p. 3 3. Chapter 1.2. Configuration of hyperspectral and multispectral imaging systems - p. 17 4. Algorithms and methods - p. 34 5. Chapter 2.1. Preprocessing of hyperspectral and multispectral images - p. 37 6. Chapter 2.2. Hyperspectral compression - p. 55 7. Chapter 2.3. Pansharpening - p. 69 8. Chapter 2.4. Unsupervised exploration of hyperspectral and multispectral images - p. 93 9. Chapter 2.5. Multivariate curve resolution for hyperspectral image analysis - p. 115 10. Chapter 2.6. Nonlinear spectral unmixing - p. 151 11. Chapter 2.7. Variability of the endmembers in spectral unmixing - p. 167 12. Chapter 2.8. An overview of regression methods in hyperspectral and multispectral imaging - p. 205 13. Chapter 2.9. Classical least squares for detection and classification - p. 231 14. Chapter 2.10. Supervised classification methods in hyperspectral imaging—recent advances - p. 247 15. Chapter 2.11. Fusion of hyperspectral imaging and LiDAR for forest monitoring - p. 281 16. Chapter 2.12. Hyperspectral time series analysis: hyperspectral image data streams interpreted by modeling known and unknown variations - p. 305 17. Chapter 2.13. Statistical biophysical parameter retrieval and emulation with Gaussian processes - p. 333 18. Application fields - p. 368 19. Chapter 3.1. Applications in remote sensing—natural landscapes - p. 371 20. Chapter 3.2. Applications in remote sensing—anthropogenic activities - p. 411 21. Chapter 3.3. Hyperspectral imaging in crop fields: precision agriculture - p. 453 22. Chapter 3.4. Food and feed production - p. 475 23. Chapter 3.5. Hyperspectral imaging for food-related microbiology applications - p. 493 24. Chapter 3.6. Hyperspectral imaging in medical applications - p. 523 25. Chapter 3.7. Hyperspectral imaging as a part of pharmaceutical product design - p. 567 26. Chapter 3.8. Hyperspectral imaging for artworks investigation - p. 583 27. Chapter 3.9. Growing applications of hyperspectral and multispectral imaging - p. 605 28. Author Index - p. 631 29. Subject Index - p. 675
