A lossless compression algorithm for hyperspectral data

In this paper, which is an expository account of a lossless compression techniques that have been developed over the course of a sequence of papers and talks, we have sought to identify and bring out the key features of our approach to the efficient compression of hyperspectral satellite data. In particular we provide the motivation for using our approach, which combines the advantages of a clustering with linear modeling. We will also present a number of visualizations which help clarify why our approach is particularly effective on this dataset. At each stage, our algorithm achieves an efficient grouping of the data points around a relatively small number of lines in a very large dimensional data space. The parametrization of these lines is very efficient, which leads to efficient descriptions of data points. Our method, which we are continuing to refine and tune, has to date yielded compression ratios that compare favorably with what is currently achievable by other approaches. A data sample consisting of an entire day's worth of global AQUA-EOS AIRS Level 1A counts (mean 12.9 bit-depth) data was used to evaluate the compression algorithm. The algorithm was able to achieve a lossless compression ratio on the order of 3.7 to 1.