Ghamisi, PedramSepehrband, FarshidKumar, LalitCouceiro, MicaelM. L. Martins, Fernando2023-09-292023-09-292013http://hdl.handle.net/10400.26/46877Remote sensing sensors generate useful information about climate and the Earth’s surface, and are widely used in resource management, agriculture, and environmental monitoring. Compression of the RS data helps in long-term storage and transmission systems. Lossless compression is preferred for high-detail data, such as from remote sensing. In this paper, a less complex and efficient lossless compression method for images is introduced. It is based on improving the energy compaction ability of prediction models. The proposed method is applied to image processing, RS grey scale images, LiDAR rasterized data, and hyperspectral images. All the results are evaluated and compared with different lossless JPEG and a lossless version of JPEG2000, thus confirming that the proposed lossless compression method leads to a high speed transmission system because of a good compression ratio and simplicity.engremote sensinglossless compressionLiDAR technologyhyperspectral imagesenhanced DPCM transformjournal article10.2306/scienceasia1513-1874.2013.39.546