Advanced methods for onboard lossless compression of hyperspectral data

This paper reports about the main results of a project aimed at developing advanced methods for lossless compression of hyperspectral data and at providing implementations on a space-certified processing board. In particular, adaptive DPCM methods exploiting 3D spectral correlation and context-based entropy coding are compared. The algorithms considered utilize "classified" DPCM, where predictors are preliminarily calculated, taking into account the statistical properties of the image being compressed, and then adaptively selected or combined. Starting from a few advanced algorithms recognized as the most relevant, a scheme suitable for onboard implementation has been derived and implemented on a TSC21020 board. The final method developed represents a good compromise between compression results and computational complexity and utilizes a CCSDS Rice encoder. Performances are assessed by means of comparisons with the results obtained by both standard and advanced algorithms providing state-of-the-art and top performances, respectively.