Transform coding techniques for lossy hyperspectral data compression

Transform-based lossy compression has a huge potential for hyperspectral data reduction. Hyperspectral data are three-dimensional, and the nature of their correlation is different in each dimension. This calls for a careful design of the 3D transform to be used for compression. In this paper we investigate the transform design and rate allocation stage for lossy compression of hyperspectral data. Firstly, we select a set of 3D transforms, obtained by combining in various ways wavelets, wavelet packets, the discrete cosine transform, and the Karhunen-Loeve transform (KLT), and evaluate the coding efficiency of these combinations. Secondly, we propose a low-complexity version of the KLT, in which complexity and performance can be balanced in a scalable way, allowing one to design the transform that better matches a specific application. Thirdly, we integrate this, as well as other existing transforms, in the framework of Part 2 of the JPEG 2000 standard, taking advantage of the high coding efficiency of JPEG 2000, and exploiting the interoperability of an international standard. We introduce an evaluation framework based on both reconstruction fidelity and impact on image exploitation, and evaluate the proposed algorithm by applying this framework to AVIRIS scenes. It is shown that the scheme based on the proposed low-complexity KLT significantly outperforms previous schemes as to rate-distortion performance. As for impact on exploitation, we consider multi-class hard classification, spectral unmixing, binary classification, and anomaly detection as benchmark applications.