DescriptionTechniques and Applications of Hyperspectral Image Analysis gives an introduction to the field of image analysis using hyperspectral techniques, and includes definitions and instrument descriptions. Other imaging topics that are covered are segmentation, regression and classification. The book discusses how high quality images of large data files can be structured and archived. Imaging techniques also demand accurate calibration, and are covered in sections about multivariate calibration techniques. The book explains the most important instruments for hyperspectral imaging in more technical detail. A number of applications from medical and chemical imaging are presented and there is an emphasis on data analysis including modeling, data visualization, model testing and statistical interpretation.
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List of Contributors.
List of Abbreviations.
1 Multivariate Images, Hyperspectral Imaging: Background and Equipment (Paul L. M. Geladi, Hans F. Grahn and James E. Burger).
2 Principles of Multivariate Image Analysis (MIA) in Remote Sensing, Technology and Industry (Kim H. Esbensen and Thorbjørn T. Lied).
3 Clustering and Classification in Multispectral Imaging for Quality Inspection of Postharvest Products (Jacco C. Noordam and Willie H. A. M. van den Broek).
4 Self-modeling Image Analysis with SIMPLISMA (Willem Windig, Sharon Markel and Patrick M. Thompson).
5 Multivariate Analysis of Spectral Images Composed of Count Data (Michael R. Keenan).
6 Hyperspectral Image Data Conditioning and Regression Analysis (James E. Burger and Paul L. M. Geladi).
7 Principles of Image Cross-validation (ICV): Representative Segmentation of Image Data Structures (Kim H. Esbensen and Thorbjørn T. Lied).
8 Detection, Classification, and Quantification in Hyperspectral Images Using Classical Least Squares Models (Neal B. Gallagher).
9 Calibration Standards and Image Calibration (Paul L. M. Geladi).
10 Multivariate Movies and their Applications in Pharmaceutical and Polymer Dissolution Studies (Jaap van der Weerd and Sergei G. Kazarian).
11 Multivariate Image Analysis of Magnetic Resonance Images: Component Resolution with the Direct Exponential Curve Resolution Algorithm (DECRA) (Brian Antalek, Willem Windig and Joseph P. Hornak).
12 Hyperspectral Imaging Techniques: an Attractive Solution for the Analysis of Biological and Agricultural Materials (Vincent Baeten, Juan Antonio Fernández Pierna and Pierre Dardenne).
13 Application of Multivariate Image Analysis in Nuclear Medicine: Principal Component Analysis (PCA) on Dynamic Human Brain Studies with Positron Emission Tomography (PET) for Discrimination of Areas of Disease at High Noise Levels (Pasha Razifar and Mats Bergström).
14 Near Infrared Chemical Imaging: Beyond the Pictures (E. Neil Lewis, Janie Dubois, Linda H. Kidder and Kenneth S. Haber).