ABSTRACT: Remote Sensing (RS) sensors generate useful information about climate and the earth surface, and are widely used in resource management, agriculture, environmental monitoring, etc.
Compression of the RS data helps in long-term storage and transmission systems. Lossless compression is preferred for high-detail data, such as RS. In this paper, simplicity of prediction models for image transformation is used to introduce a less complex and efficient lossless compression method for Remote Sensing images based on improving the energy compaction ability of prediction models. Further, the proposed method is applied on different types of test cases including: image processing test cases, RS grey scale images, LiDAR rasterized data and Hyperspectral images, and the results are evaluated and compared with different JPEG standard methods such as lossless JPEG and lossless version of JPEG2000. Results confirm that the proposed lossless compression method leads to a high speed transmission system because of a good compression ratio and simplicity. The proposed method has a high potential to be used in real time processing.
KEYWORDS: Remote Sensing (RS), Lossless Compression, LiDAR Technology, Hyperspectral Images, Enhanced DPCM Transform.