A Low-Rank Tensor Decomposition Based Hyperspectral Image Compression Algorithm

  • Mengfei Zhang
  • Bo Du
  • Lefei ZhangEmail author
  • Xuelong Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9916)


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.


HSI Compression Reconstruction Tensor representation Low-rank decomposition 



This work was supported by the National Natural Science Foundation of China under Grants 61401317, 61471274, 91338111, and U1536204.


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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.School of ComputerWuhan UniversityWuhanChina
  2. 2.Center for OPTIMAL, State Key Laboratory of Transient Optics and Photonics, Xi’an Institute of Optics and Precision MechanicsChinese Academy of SciencesXi’anChina

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