Principles of Multivariate Image Analysis (MIA) in Remote Sensing, Technology and Industry

Techniques and Applications of Hyperspectral Image Analysis

Volumn , Issue , 2007, Pages 17-42

  Esbensen, Kim H ^a   Lied, Thorbjørn T ^b   Lowell, Kim ^c   Edwards, Geoffrey ^c  

^a AALBORG UNIVERSITY   (Denmark)

^b KONGSBERG MARITIME   (Norway)

^c NONE

Author keywords

Colour slicing technique; Montmorency experimental forest; Multi normal distributions; Multivariate image analysis (MIA) principles; Problem specific combination; Spatio temporal analysis; Suboptimal class representation; Tomographic and chemical imaging; Traditional image analysis; Traditional pixel classifiers

Indexed keywords

EID: 79960085192     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1002/9780470010884.ch2     Document Type: Chapter

References (11)

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2. Esbensen, K. H., Geladi, P. and Grahn, H. (1992) Strategies for multivariate image regression (MIR). Chemometrics and Intelligent Laboratory Systems, 14, 67-86.
3. Esbensen, K. H., Edwards, G. and Eldridge, N. R. (1993) Multivariate image analysis in forestry applications involving high resolution airborne imagery. In: 8th Scandinavian Conference on Image Analysis. NOBIM, Tromsø, pp. 953-963.
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10. McColl, W. D., Till, S. M. and Neville, R. A. (1984) MEIS II operational sensor for multidisciplinary studies. In: 9th Canadian Symposium on Remote Sensing, pp. 497-501.
11. Mortensen, P. P. and Esbensen K. H. (2005) Optimisation of an image analytical system for bulk materials using the Theory of Sampling. In: Holmes, R. (Ed.), Proceedings of the 2nd World Conference on Sampling and Blending. AUSIMM, Brisbane, pp. 45-54.