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

Abstract

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 on multi-spectral images, we conducted statistical evaluation on datasets consisting of hundreds of granules from both geostationary and polar imagers. We broke these datasets up by different criteria such as hemisphere, season, and time-of-day in order to ensure the results are robust, reliable, and applicable for future imagers.