A Low-Rank Tensor Decomposition Based Hyperspectral Image Compression Algorithm

Hyperspectral image (HSI), which is widely known that contains much richer information in spectral domain, has attracted increasing attention in various fields. In practice, however, since a hyperspectral image itself contains large amount of redundant information in both spatial domain and spectral domain, the accuracy and efficiency of data analysis is often decreased. Various attempts have been made to solve this problem by image compression method. Many conventional compression methods can effectively remove the spatial redundancy but ignore the great amount of redundancy exist in spectral domain. In this paper, we propose a novel compression algorithm via patch-based low-rank tensor decomposition (PLTD). In this framework, the HSI is divided into local third-order tensor patches. Then, similar tensor patches are grouped together and to construct a fourth-order tensor. And each cluster can be decomposed into smaller coefficient tensor and dictionary matrices by low-rank decomposition. In this way, the redundancy in both the spatial and spectral domains can be effectively removed. Extensive experimental results on various public HSI datasets demonstrate that the proposed method outperforms the traditional image compression approaches.