A rough-GA based optimal feature selection in attribute profiles for classification of hyperspectral imagery

Morphological attribute profiles are robust in capturing the spectral–spatial information of hyperspectral imagery. To incorporate maximum spatial information, generation of a profile using multiple attributes with large number of threshold values is a well-known approach. Although the profile contains very rich spatial information, at the same time its dimensionality increases. This raises two critical problems for hyperspectral image classification: (i) curse of dimensionality and (ii) computational complexity. To mitigate such problems, the only supervised feature selection technique that exists in the literature is computationally demanding. In this article, a fast supervised feature selection technique by exploiting rough set theory and genetic algorithms is proposed. Our technique computes the relevance and significance of each feature in the profile using rough set theory. Then, based on the relevance and significance values a novel fitness function of genetic algorithms is designed to select an optimal subset of features from the constructed profile. To show the effectiveness of the proposed technique, it is compared with the existing state-of-the-art technique by considering three real hyperspectral data sets.