Lossless Compression Studies for NOAA GOES-R Hyperspectral Environmental Suite

This paper presents a systematic study of lossless compression techniques for the next-generation NOAA GOES-R Hyperspectral Environmental Suite (HES). The lossless compression results are obtained and compared from, a) transform-based (e.g. JPEG2000, 3D SPIHT) b) prediction-based (e.g. JPEG-LS, CALIC) c) projection-based (e.g. Lossless PCA, Optimized Orthogonal Matching Pursuit based Linear Prediction) and d) clustering-based (e.g. PPVQ, FPVQ) methods. The ultraspectral sounder data features strong correlations in disjoint spectral regions due to the same type of absorbing gases. Robust data preprocessing schemes (e.g. BAR, MST reordering) are also demonstrated to improve compression gains of existing state-of-the-art compression methods such as JPEG2000, 3D SPIHT, JPEG-LS, and CALIC.