Regularized set-to-set distance metric learning for hyperspectral image classification

Abstract

Combining the spectral and spatial information, we propose a regularized set-to-set distance metric learning method (RSSDML) for the hyperspectral image (HSI) classification. It first performs a local pixel neighborhood preserving embedding to reduce the dimensionality and meanwhile to preserve the local similarity structures of HSI, and then puts each target spectral pixel and its spatial neighbors into a set, and measures the distance between different sets to reveal the overall differences of different target spectral pixels. In the computation of the set-to-set distance, a regularization strategy is used to differentiate individual pixels in a pixel set and to improve the set-based metric relations. Exploiting both the correlations between neighboring pixels in a pixel set and the similarities between different pixel sets, the proposed RSSDML dramatically improves traditional point-based and set-based metric learning methods and provides better classification results than some state-of-the-art spatial-spectral classifiers on two benchmark hyperspectral data sets.