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19 October 2017 Maximum auto-mutual-information factor analysis (Conference Presentation)
Allan A. Nielsen
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Proceedings Volume 10427, Image and Signal Processing for Remote Sensing XXIII; 104270J (2017) https://doi.org/10.1117/12.2278290
Event: SPIE Remote Sensing, 2017, Warsaw, Poland
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.
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Allan A. Nielsen "Maximum auto-mutual-information factor analysis (Conference Presentation)", Proc. SPIE 10427, Image and Signal Processing for Remote Sensing XXIII, 104270J (19 October 2017); https://doi.org/10.1117/12.2278290
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KEYWORDS
Remote sensing
Statistical analysis
Information theory
Principal component analysis
Detection and tracking algorithms
Image analysis
Machine learning
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