Abstract: Some key issues related to space-based compression design are discussed. Various system considerations as well as potential compression options are also presented. A brief overview of a previously-reported robust lossy transform coding algorithm is given followed by the study of its performance sensitivities. These sensitivities include 1) performance sensitivity to commonly observed anomalies in the data including band misalignment and dead/saturated pixels, 2) impact of geometric distortion on compression performance, 3) performance sensitivity to different grouping of bands for spectral decorrelation, and 4) impact of compression on spectral fidelity. In addition, the impact of compression on the result of exploitation of environment data including automated cloud study will be considered. It is shown that preprocessing to correct for geometric distortion noticeably improve the compression performance. Difference grouping of bands also influences the performance. The loss of the spectral fidelity, as measured by the deviation from the original correlation coefficient matrix, is very insignificant regardless of the image nd the coding bit rate. For the available bit rate, it is possible to trade off the compressions-induced error between the spectral and spatial resolutions. In the investigated implementation scenarios, it was found that compression at rates approaching 16 to 1 has minor impact on the exploitation and assessment of the ultimate derived automated cloud analysis. Additional work is needed to evaluate the impact of compression on other products, such as sea surface temperature. The results to date suggest that lossy compression may have a role in efficient transmission of environmental information and in their subsequent exploitation. !23
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