A sparse tensor-based classification method of hyperspectral image

Previous studies have demonstrated that spatial information can provide significant improvement for the accuracy hyperspectral image (HSI) classification. However, it remains a challenging task to extract three-dimensional (3-D) features from HSI directly. In this paper, a sparse tensor-based classification (STC) method for HSI is proposed. Different from the traditional vector-based or matrix-based methods, the STC utilizes tensor technique to extract the joint spatial-spectral tensor features. We exploit the principal component analysis (PCA) and the 3-D intrinsic spatial-spectral tensors of HSI to alleviate within-class spatial-spectral variation, and to improve the classification performance simultaneously. First, the HSI is segmented into a number of overlapping 3-D tensor patches, which are modelled as summation of intrinsic spatial-spectral tensors and corresponding variation tensors in the next step. Second, the intrinsic spatial-spectral tensor is decomposed into three matrices and a core tensor by the Tucker decomposition (TKD). Sparsity constraint is enforced on the core tensor to extract joint sparse spatial-spectral features. We utilize tensor-based dictionary learning algorithm to train three dictionaries, in order to extract more discriminative tensor features for classification. Finally, we use the support vector machine (SVM) to perform the pixel-wise classification. Experimental results on real HSI datasets demonstrate the proposed method can achieve accurate and robust classification results, and can provide competitive results to state-of-the-art methods. (C) 2019 Elsevier B.V. All rights reserved.