Hyperspectral Image Denoising Based on Tensor Group Sparse Representation

doi:10.11947/j.AGCS.2017.20150403

Abstract

Abstract: A novel algorithm for hyperspectral image (HSI) denoising is proposed based on tensor group sparse representation. A HSI is considering as 3 order tensor. First, a HSI is divided into small tensor blocks. Second, similar blocks are gathered into clusters, and then a tensor group sparse representation model is constructed based on every cluster. Through exploiting HSI spectral correlation and nonlocal similarity over space, the model constrained tensor group sparse representation can be decomposed into a series of unconstrained low-rank tensor approximation problems, which can be solved using the tensor decomposition technique. The experiment results on the synthetic and real hyperspectral remote sensing images demonstrate the effectiveness of the proposed approach.

Key words: hyperspectral image, tensor, sparse representation, nonlocal similarity

CLC Number:

P236

WANG Zhongmei, YANG Xiaomei, GU Xingfa. Hyperspectral Image Denoising Based on Tensor Group Sparse Representation[J]. Acta Geodaetica et Cartographica Sinica, 2017, 46(5): 614-622.

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