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Linear Feature Extraction for Hyperspectral Images Based on Information Theoretic Learning

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2 Author(s)
Mehdi Kamandar ; Faculty of Electrical and Computer Engineering, Tarbiat Modares University , Tehran, Iran ; Hassan Ghassemian

This letter proposes a new supervised linear feature extractor for hyperspectral image classification. The criterion for feature extraction is a modified maximal relevance and minimal redundancy (MRMD), which has been used for feature selection until now. The MRMD is a function of mutual information terms, which possess higher order statistics of data; thus, it is effective for hyperspectral data with informative higher order statistics. The batch and stochastic versions of the gradient ascent are performed on the MRMD to find the optimal parameters of a linear feature extractor. Preliminary results achieve better classification performance than the traditional methods based on the first- and second-order moments of data.

Published in:

IEEE Geoscience and Remote Sensing Letters  (Volume:10 ,  Issue: 4 )