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Classification techniques applied to hyperspectral images are very useful for lithologic discrimination and geological mapping. Classifiers are often applied either to all spectral channels or only to absorption spectral channels. However, it is difficult to obtain different lithology information using specific absorption regions from the narrow bandwidth and contiguous spectral channels due to spectral variability among rocks. In this article, we propose a band selection (BS) method for hyperspectral lithologic discrimination, in which the lithological superpixels are first gathered. A spectral bands selection criterion is learned by measuring the homogeneity and the variation of the lithological superpixels, and lithologic discriminating bands are identified by an efficient clustering algorithm based on affinity propagation. In this article, two geologic test sites, i.e., the Airborne Visible/Infrared Imaging Spectrometer data of the Cuprite, Nevada, USA, including 11 lithologic units (9 types of rocks) and the Hyperion data of Junggar, China, with 5 lithologic units, are chosen for validation. The performance of the proposed BS method is compared with those of using all the bands, specific absorption spectral channels, and two literature BS techniques. Experimental results show that the proposed method improves mapping accuracy by selecting fewer bands with higher lithologic discrimination capability than the other considered methods.

Abstract:Traditional supervised band selection (BS) methods mainly consider reducing the spectral redundancy to improve hyperspectral imagery (HSI) classification with class labels and pairwise constraints. A key observation is that pixels spatially close to each other in HSI have probably the same signature, while pixels further away from each other in the space have a high probability of belonging to different classes. In this paper, we propose a novel discriminative feature metric-based affinity propagation (DFM-AP) technique where the spectral and the spatial relationships among pixels are constructed by a new type of discriminative constraint. This discriminative constraint involves chunklet and discriminative information, which are introduced into the BS process. The chunklet information allows for grouping of spectrally-close and spatially-close pixels together without requiring explicit knowledge of their class labels, while discriminative information provides important separability information. A discriminative feature metric (DFM) is proposed with the discriminative constraints modeled in terms of an optimal criterion for identifying an efficient distance metric learning method, which involves discriminative component analysis (DCA). Following this, the representative subset of bands can be identified by means of an exemplar-based clustering algorithm, which is also known as the process of affinity propagation. Experimental results show that the proposed approach yields a better performance in comparison with several representative class label and pairwise constraint-based BS algorithms. The proposed DFM-AP improves the classification performance with discriminative constraints by selecting highly discriminative bands with low redundancy.

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