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Hyperspectral Imaging: Data Handling in Science and Technology

Editat de Jose Manuel Amigo, Giulia Gorla
en Limba Engleză Paperback – 2027
Hyperspectral Imaging, Second Edition builds on the foundational insights of the first edition, covering core analytical frameworks and introducing new algorithms, hardware technologies, and cutting-edge applications. By incorporating the latest research findings and case studies, sections provide readers with enhanced tools and techniques for effective hyperspectral and multispectral image analysis across diverse scientific, industrial, and field domains. The book also addresses evolving challenges and fresh opportunities in the field, ensuring that users are equipped with the most current knowledge and practices. It underscores the continued relevance and transformative potential of hyperspectral and multispectral imaging in contemporary research and industry.

Graduate students, academics, early researchers, and 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, and more will find this update extremely useful.

  • Provides a comprehensive roadmap of hyperspectral and multispectral image analysis, including benefits and considerations for each method discussed
  • Covers state-of-the-art applications in different scientific fields
  • Discusses the implementation of hyperspectral devices in different environments
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Specificații

ISBN-13: 9780443438929
ISBN-10: 0443438927
Pagini: 1190
Dimensiuni: 152 x 229 mm
Ediția:2
Editura: ELSEVIER SCIENCE
Seria Data Handling in Science and Technology


Cuprins

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