Performance Improvement on k ²-Raster Compact Data Structure for Hyperspectral Scenes
Kevin Chow, Dion Eustathios Olivier Tzarmarias, Miguel Hernández-Cabronero, Ian Blanes, Joan S. Serra-Sagrista · IEEE Geoscience and Remote Sensing Letters · 2021
This letter proposes methods to improve data size and access time for$k^{2}$-raster, a losslessly compressed data structure that provides efficient storage and real-time processing. Hyperspectral scenes from real missions are used as our testing data. In previous studies, with$k^{2}$-raster, the size of the hyperspectral data was reduced by up to 52% compared with the uncompressed data. In this letter, we continue to explore novel ways of further reducing the data size and access time. First, we examine the possibility of using the raster matrix of hyperspectral data without any padding (unpadded matrix) while still being able to compress the structure and access the data. Second, we examine some integer encoders, more specifically the Simple family. We discuss their ability to provide random element access and compare them with directly addressable codes (DACs), the integer encoder used in the original description for$k^{2}$-raster. Experiments show that the use of unpadded matrices has improved the storage size up to 6% while the use of a different integer encoder reduces the storage size up to 6% and element access time up to 20%.