CP tensor-based compression of hyperspectral images

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-zero entries. In addition, non-zero entries of the R rank-1 tensors are sparse and follow a regular distribution. 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. (C) 2017 Optical Society of America