Hyperspectral image, video compression using sparse tucker tensor decomposition

Hyperspectral image and videos provide rich spectral information content, which facilitates accurate classification, unmixing, temporal change detection, and so on. However, with the rapid improvements in technology, the data size has increased many folds. To properly handle the enormous data volume, efficient methods are required to compress the data. This paper proposes a multi-way approach for compression of the hyperspectral image or video sequence. In this approach, a differential representation of the data is first obtained. In the case of hyperspectral images, the difference between consecutive bands is obtained and in case of videos, the difference between consecutive frames is computed. In the next step, a sparse Tucker tensor decomposition is performed and the sparse core tensor obtained. Finally, the core tensor and the corresponding factor matrices are truncated and the data encoded to obtain the compressed version for transmission. The compression method utilises the multi-way structure of the data and hence can be extended for hyperspectral videos. Experimental results on several real data imply that the proposed compression approach obtains better efficiency in terms of compression ratio, signal to noise ratio.

Automated summary

This paper proposes a multi-way approach for compression of hyperspectral image or video sequence, obtaining better efficiency in terms of compression ratio and signal to noise ratio through differential representation and sparse Tucker tensor decomposition.