Unsupervised Data Driven Feature Extraction by Means of Mutual Information Maximization

Abstract

In Earth observations technical literature, several methods have been proposed and implemented to efficiently extract a proper set of features for classification and segmentation purposes. However, these architectures show drawbacks when the considered datasets are characterized by complex interactions among the samples, especially when they rely on strong assumptions on noise and label domains. In this paper, a new unsupervised approach for feature extraction, based on data driven discovery, is introduced for accurate classification of remotely sensed data. Specifically, the proposed architecture exploits mutual information maximization in order to retrieve the most relevant features with respect to information measures. Experimental results on real datasets show that the proposed approach represents a valid framework for feature extraction from remote sensing images.