The correlation-based tucker decomposition for hyperspectral image compression

Tucker decomposition (TD) is widely used in hyperspectral image (HSI) processing. Generally, the performance of TD-based method depends on the core tensor and factor matrices, while the construction of core tensor and factor matrices is still a research topic. We give the detailed discussion about the correlation and performance of TD-based methods in this paper. Since TD is solved by singular value decomposition (SVD), the construction of core tensor and factor matrices should be determined by the distribution of singular energy of each mode-n matricization. Depending on the discussion, we propose a correlation-based Tucker decomposition (CBTD) method to construct the core tensor and factor matrices. As a general method, this proposed CBTD can be employed in any TD-based method of Nth-order ten sor. The analysis on real HSI data verifies our conclusion about correlation and good performance of CBTD. Besides, the proposed CBTD method has better ability to improve the performance of HSI compression than other state-of-the-art methods. (c) 2020 Elsevier B.V. All rights reserved.

Automated summary

Tucker decomposition is widely used in hyperspectral image processing, and this paper discusses the correlation and performance of TD-based methods, proposing a correlation-based Tucker decomposition method which shows better ability to improve the compression performance of HSI.