Hyperspectral Imaging

2. Auflage 2027
ISBN: 978-0-443-43892-9
Verlag: Elsevier Science

Buch, Englisch, 1190 Seiten, Format (B × H): 152 mm x 229 mm

Hyperspectral Imaging, Second Edition builds on the foundational insights of the first whilst reflecting the rapid advancements in hyperspectral and multispectral imaging technologies and methodologies in three directions: hardware, software, and applications. This heavily updated and expanded book not only covers the core analytical frameworks but also introduces new algorithms, hardware technology and cutting-edge applications that have emerged since the first edition. By incorporating the latest research findings and case studies, the contributions provide readers with enhanced tools and techniques for effective hyperspectral and multispectral image analysis across diverse scientific, industrial and field domains. Additionally, the book once again addresses evolving challenges and fresh opportunities in the field, ensuring that users are equipped with the most current knowledge and practices. This comprehensive update underscores the continued relevance and transformative potential of hyperspectral and multispectral imaging in contemporary research and industry. Hyperspectral Imaging, Second Edition is written for graduate students, academics and early researchers as well as industry scientists across various disciplines working with hyperspectral and multispectral images, including analytical chemistry, remote sensing, vegetation and crops, food and feed production, forensic sciences, biochemistry, medical imaging, pharmaceutical production, art studies, cultural heritage, among other topics.

Autoren/Hrsg.

Gorla, Giulia

Fachgebiete

• Technische Wissenschaften Sonstige Technologien | Angewandte Technik Signalverarbeitung, Bildverarbeitung, Scanning
• Geowissenschaften Geologie Geodäsie, Kartographie, Fernerkundung
• Geowissenschaften Geographie | Raumplanung Geodäsie, Kartographie, GIS, Fernerkundung

Inhaltsverzeichnis

Section I: Introduction
1. Hyperspectral and multispectral imaging: setting the scene
2. New hardware achievements
3. Types of Hyperspectral images and configuration of the measurements

Section II: Algorithms
4. Spectral and Spatial Pre-processing of hyperspectral and multispectral images
5. Pansharpening
6. Compression (including randomization)
7. Unsupervised exploration and clustering of hyperspectral and multispectral images
8. Multivariate curve resolution (Spectral Unmixing) for hyperspectral image analysis
9. Nonlinear Spectral Unmixing
10. Variability of the endmembers in spectral unmixing
11. An overview of regression methods in hyperspectral and multispectral imaging
12. Target Anomaly Detection methods
13. Supervised classification methods in hyperspectral imaging—recent advances
14. Fusion of hyperspectral images. A comprehensive perspective
15. Fusion of hyperspectral imaging and LiDAR for forest monitoring
16. Hyperspectral time series analysis: hyperspectral image data streams interpreted by modelling known and unknown variations
17. Statistical biophysical parameter retrieval and emulation with Gaussian processes
18. Hyperspectral super-resolution
19. Deep Learning in Hyperspectral Imaging
20. Spatial and spectral Limits of Detection

Section III: Recent Developments in the Applied Field
21. Different applications require different hyperspectral systems and different chemometric methodologies
22. Applications in remote sensing: natural landscapes
23. Applications in remote sensing: anthropogenic activities
24. Hyperspectral imaging in crop fields: precision agriculture
25. Food and feed production
26. Hyperspectral imaging for food-related microbiology applications
27. Hyperspectral imaging in medical applications
28. Hyperspectral imaging as a part of pharmaceutical product design
29. Hyperspectral imaging for artwork investigation
30. Industrial Hyperspectral Applications
31. Plastics in the environment
32. Forensic sciences
33. Planetary Science and Hyperspectral Imaging
34. Growing applications of hyperspectral and multispectral imaging

Section IV: Programming
35. A brief introduction to available hyperspectral image datasets and software that have been used in this book
36. Programming in Matlab
37. Programming in R
38. Programming in Python

Über Autor(innen)

Gorla, Giulia
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.