A Comparative Study of Lossless Compression Algorithms on Multi-spectral Imager Data

High resolution multi-spectral imagers are becoming increasingly important tools for studying and monitoring the earth. As much of the data from these multi-spectral imagers is used for quantitative analysis, the role of lossless compression is critical in the transmission, distribution, archiving, and management of the data. To evaluate the performance of various compression algorithms, we used data from the geostationary spinning enhanced visible and infrared imager (SEVIRI), and from the polar moderate resolution imaging spectroradiometer (MODIS) and advanced very high resolution radiometer (AVHRR). Since considering a small number of samples or focusing exclusively on the mean of the data is insufficient for use in planning engineering requirements, we conducted statistical evaluation on datasets consisting of hundreds of granules from each imager. We broke these datasets up by different criteria in order to ensure the results are robust, reliable, and applicable for future imagers. For example, the data for MODIS was examined by hemisphere, by season, and by platform (Aqua and Terra). The MODIS results indicated that the compression performance rank is fairly consistent across platform, hemisphere, and season, but there is some variation in the relative performance for different spatial resolutions.