CP tensor-based compression of hyperspectral

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文件名称：CP tensor-based compression of hyperspectral
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更新时间：2021-01-16 07:55:28
CP; tensor; HSI; compression 这是一篇关于张量分解的文章 In this paper, an effective CANDECOMP/PARAFAC tensor-based compression (CPTBC) approach is proposed for on-ground hyperspectral images (HSIs). By considering the observed HSI cube as a whole three-order tensor, the proposed CPTBC method utilizes the CANDECOMP/PARAFAC tensor decomposition to decompose the original HSI data into the sum of R rank-1 tensors, which can simultaneously exploit both the spatial and spectral information of HSIs. Specifically, compared with the original HSI data, the R rank-1 tensors have fewer non-zeroentries.Inaddition,non-zeroentriesoftheR rank-1tensorsaresparseandfollowaregulardistribution. Therefore, the HSI can be efficiently compressed into R rank-1 tensors with the proposed CPTBC method. Our experimental results on real three HSIs demonstrate the superiority of the proposed CPTBC method over several well-known compression approaches and the average PSNR improvements of the proposed method over the six compared methods (i.e., MPEG4, band-wise JPEG2000, TD, 3D-SPECK, 3D-TCE, 3D-TARP) are more than 13, 10, 6, 4, 3, and 3 dB, respectively.