Research On A Novel Lossless Compression Algorithm For Multi-spectral Remote Sensing Image

Remote sensing information processing technology has been used widely and played a significant role in the fields of national economic and people’s livelihood, such as military, geology, environmental monitoring and meteorology in recent years.At the same time, with the development of multi-spectral imaging technology, the spatial and temporal resolution of multi-spectral remote sensing images obtained by remote sensing satellites have been greatly improved. Moreover, the technique is able to provide the fine details, and the improvement of image quality further promotes the application of multi-spectral remote sensing images. Multi-spectral remote sensing images generally include up to hundreds of spectral information in images, thus the enormous data brings a lot of inconvenience in transmission, reception, storage and its usage. Multi-spectral remote-sensing image is valuable for a long time even permanent preservation, but it is difficult to obtain because it’s need to obtain via satellites. Concerning this and avoiding the influence of further image processing and application due to the loss of any useful information, lossless data compression here is effective.The existing multi-spectral remote-sensing image compression technique is researched in this paper. On the basis of research on the existing international standard of lossless image compression, some useful attempts are carried out. This paper firstly analyzes the spectral correlation, the spatial correlation and the DWT coefficients characteristics of multi-spectral remote sensing images.And then a comprehensive encoding scheme is proposed, which combined the RCT transform to redistribute spectral information, DPCM predictor and entropy encoder based on improved RICE algorithm. In order to compare the compression effect of our scheme, a multi-spectral remote sensing image compression algorithm based on wavelet transform is proposed. The experimental results show that the program has better coding efficiency, higher implementation efficiency and lower algorithm complexity. It can be easily implemented in hardware and has error resilience to a certain degree. Moreover, it can meet the real-time demand of multi-spectral remote sensing image compression. It is an effective multi-spectral remote sensing lossless image compression method.