Low-rank group inspired dictionary learning for hyperspectral image classification

Abstract

Dictionary learning has yielded impressive results in sparse representation based hyperspectral image (HSI) classification. However, challenges remain for exploiting spectral-spatial characteristics. In this paper, we make the first attempt to classify the HSI via low-rank group inspired dictionary learning (LGIDL). Core ideas of the LGIDL are threefold: (1) super-pixel segmentation is implemented to obtain homogeneous regions, which can be viewed as spatial groups for LGIDL; (2) non-negative low-rank coefficient and dictionary are updated alternatively in the optimization problem of LGIDL. The low-rank group prior helps to seek lowest-rank representation of a collection of data samples jointly. Pixels in the same group share common low-rank pattern, which facilitates the integration of spectral-spatial information; (3) the low-rank coefficients of test samples are adopted to determine the corresponding class labels in linear support vector machine (SVM). Experimental results demonstrate that the LGIDL achieves better performance to the state-of-the-art HSI classification methods on several challenging datasets even with small labeled samples.