A New Low Complexity KLT for Lossy Hyperspectral Data Compression

Transform-based lossy compression has a huge po- tential for hyperspectral data reduction. In this paper we propose a lossy compression scheme for hyperspectral data based on a new low-complexity version of the Karhunen-Lo` eve transform, in which complexity and performance can be balanced in a scalable way, allowing one to choose the best trade off that better matches a specific application. Moreover, we integrate this transform 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. Hyperspectral imaging amounts to collecting the energy reflected or emitted by ground targets at a typically very high number of wavelengths, resulting in a data cube consisting of tens to hundreds of bands. These data have become increas- ingly popular, since they enable plenty of new applications, in- cluding detection and identification of surface and atmospheric constituents present, analysis of soil type, agriculture and forest monitoring, environmental studies and military surveil- lance. The data are usually acquired by a remote platform (a satellite or an aircraft), and then downlinked to a ground sta- tion. Due to the huge size of the datasets, compression is nec- essary to match the available transmission bandwidth. In the past, scientific data have been almost exclusively compressed by means of lossless methods, in order to preserve their full quality. However, more recently, there has been an increasing interest in their lossy compression. Many of these techniques are based on decorrelating transforms, in order to exploit spatial and inter-band (i.e., spectral) correlation, followed by a quantization stage and an entropy coder. Examples include the