Principal component analysis of multivariate images
- Authors
- Paul L. M. Geladi, Hans Isaksson, Lennart Lindqvist, Svante Wold and Kim H. Esbensen
- Published
- 1989
- DOI
- 10.1016/0169-7439(89)80049-8
- Citation
- Geladi et al.: "Principal component analysis of multivariate images", Chemometrics and Intelligent Laboratory Systems, 5, 209-220, 1989.
- Abstract
- Multivariate data analysis of 2-dimensional object arrays is explained and its application to images is demonstrated. The possibility of creating soft models in latent variables is a great aid in this process. The pattern classification, cognition and recognition processes as used in the SIMCA method can be translated to latent variables of the multivariate images. A small example of a satellite image is presented. The method aspires to make maximal use of the possibilities of standard image analysis equipment and to combine the results obtained with more traditional image analysis techniques. This article concentrates mainly on theoretical aspects.
- Tags
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