Dimensionality Reduction Based on Group-Based Tensor Model for Hyperspectral Image Classification

Dimensionality reduction is one of the most important tasks in improving hyperspectral image (HSI) classification performance and has been widely studied. In traditional dimensionality reduction methods, the hyperspectral data cube is first converted to a matrix, and then, the matrix-algebra is employed, in which the spatial structure is usually not taken into consideration. To jointly utilize spectral and spatial information, researches considering HSI as a tensor have attracted more and more attention. In this letter, we propose a group-based tensor model for HIS dimensionality reduction. The local and nonlocal spatial information of HSI cubes is explored by segmenting the original HSI tensors into a lot of small tensors and grouping them into clusters. Finally, the clusters are projected into low-rank subspace to obtain a proper feature space. Experimental results on two real hyperspectral data sets exhibit the effectiveness of the proposed algorithm for HSI-dimensionality reduction.