Compression of hyperspectral images based on Tucker decomposition and CP decomposition.

Abstract

Hyperspectral imagers are developing towards high resolution, high detection sensitivity, broad spectra, and wide coverage, which means that hyperspectral data are getting more and more substantial. This brings a great challenge to data storage and real-time transmission of hyperspectral data. A compression method based on Tucker decomposition and CANDECOMP/PARAFAC decomposition (TD-CP) is proposed. The hyperspectral data are treated as a third-order tensor. First, TD is performed on the hyperspectral data to obtain a core tensor and three factor matrices, and then CP decomposition is performed on the core tensor. Compared with principal component analysis (PCA)+JPEG2000, TD, and CP, TD-CP can retain spatial information and spectral information better at the same time, and running time is shorter.