Spectral-spatial online dictionary learning for hyperspectral image classification

Abstract

Sparse representation (SR) based hyperspectral image (HSI) classification is a rapidly evolving research topic. How to construct an optimized dictionary to better characterize spectral-spatial features of HSI is an important problem. In this paper, a novel spectral-spatial online dictionary learning (SSODL) method for HSI classification is proposed. The main idea is to learn a complete and discriminative dictionary by exploiting both spatial and spectral information all over the whole image. Rather than only using training samples for dictionary construction, the online dictionary learning (ODL) mechanism can effectively improve the adaptive representation capability of different pixels. Specifically, the contextual characteristics of HSI are integrated with discriminative spectral information for the ODL, i.e., pushing similar pixels in neighborhood to share similar sparse coefficients w.r.t. the well learnt dictionary. By this way, the yielding sparse coefficients are structured and discriminative. Finally, a traditional classifier, i.e., linear support vector mechine (SVM), is applied to the sparse coefficients and the final classification results are obtained. Experimental results on real HSIs show the effectiveness of the proposed method.