Hyperspectral Imaging, Volume 32

Section 1. Introduction
1.1. Hyperspectral images. From remote sensing to bench top instruments. A general overview
1.2. Hyperspectral cameras. Types of hyperspectral cameras, radiations and sensors

Section 2. Algorithms and Methods
2.1. Pre-processing. Spatial and spectral issues
2.2. Hyperspectral data compression
2.3. Hyperspectral super-resolution
2.4. Pansharpening
2.5. Unsupervised pattern recognition methods
2.6. Multivariate curve resolution
2.7. Non-linear spectral un-mixing
2.8. Spectral un-mixing and variability of endmembers
2.9. Regression models
2.10. Classification
2.11. Mixing spatial with spectral signatures
2.12. Hyperspectral Image fusion
2.13. Hyperspectral video analysis
2.14. Advances in statistical biophysical parameter retrieval and model inversion

Section 3. Application Fields
3.1. Hyperspectral cameras adapted to the applications. How and when
3.2. Remote sensing
3.3. Vegetation and crops
3.4. Food and feed production
3.5. Biochemistry
3.6. Medical imaging
3.7. Pharmaceutical production
3.8. Artwork
3.9. Industrial hyperspectral imaging
3.10. Forensic sciences, and more

Section 4. Appendices
4.1. List of abbreviations
4.2. List of tables
4.3. List of figures

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.

• Provides a comprehensive roadmap of hyperspectral image analysis, with 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

Scientists, academics, and graduate students in various disciplines working with hyperspectral images, including remote sensing, vegetation and crops, food and feed production, forensic sciences, biochemistry, medical imaging, pharmaceutical production, and art studies