Spatial-spectral feature extraction of hyperspectral images using tensor-based collaborative graph analysis

Although the collaborative graph-based discriminant analysis (CGDA) method has shown promising performance for the feature extraction of the hyperspectral image (HSI), both the intrinsic local subspace structures and spatial structural information are ignored in CGDA. To address these problems, a novel spatial-spectral feature extraction method, i.e. tensor-based collaborative graph analysis, is proposed in this letter. Specifically, the spectral similarity is utilized to calculate the Tikhonov matrix, which can constrain the testing samples to be represented by similar training samples in the collaborative representation model. To fully exploit the 3D spatial-spectral structural information, the collaborative representation model is extended to tensor space by using the third-order tensor representation of HSI, in which samples are constructed by small local patches around the central pixels. Depending on the above techniques, the quality of the collaborative graph constructed by the coefficient matrix can be significantly improved to obtain the discriminative low-dimensional features. Experiments on three HSI data sets demonstrate the superiority of the proposed method.