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Proceedings Paper

Maximum auto-mutual-information factor analysis (Conference Presentation)
Author(s): Allan A. Nielsen

Paper Abstract

In signal and image processing principal component analysis (PCA) is often used for dimensionality reduction and feature extraction in pre-processing steps to for example classification. In remote sensing image analysis PCA is often replaced by maximum autocorrelation factor (MAF) or minimum noise fraction (MNF) analysis. This is done because MAF and MNF analyses incorporate spatial information in the orthogonalization of the multivariate data which is conceptually more satisfactory and which typically gives better results. In this contribution, autocorrelation between the multivariate data and a spatially shifted version of the same data in the MAF analysis is replaced by the information theoretical, entropy and Kullback-Leibler divergence based measure mutual information. This potentially gives a more detailed decomposition of the data. Also, the orthogonality between already found components and components of higher order requested in the MAF analysis is replaced by a requirement of minimum mutual information between components. The sketched methods are used on the well-known AVIRIS (https://aviris.jpl.nasa.gov/) Indian Pines data.

Paper Details

Date Published: 19 October 2017
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Proc. SPIE 10427, Image and Signal Processing for Remote Sensing XXIII, 104270J (19 October 2017); doi: 10.1117/12.2278290
Show Author Affiliations
Allan A. Nielsen, Technical Univ. of Denmark (Denmark)


Published in SPIE Proceedings Vol. 10427:
Image and Signal Processing for Remote Sensing XXIII
Lorenzo Bruzzone, Editor(s)

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