Researches On Hyperspectral Image Lossless Compression Algorithm Based On Prediction

In the earth-observing remote sensing systems, hyperspectral remote sensing images are important data sources,which have widespread military civil applications. Due to their huge magnitude of data,it is necessary to compression hyperspectral images(HIS) in applications. As 3-D images,hyperspectral images are different from general 2-D images. They have both spatial correlations and spectral correlations. And in which the spectral correlations are stronger than spatial correlations. In this paper the algorithms for hyperspectral remote sensing images are proposed.From the correlation view, characteristics of hyperspectral image are analyzed. Including spatial correlation, spectral correlation, the information entropy analysis. Firstly,the three kinds of general compression algorithms for hyperspectral images are presented,which are transform compression,vector quantization and prediction compression algorithm. Hyperspectral remote sensing images with transmission of non-repeatable and valuable, and general the prediction algorithm used only in the removal of two-dimensional image spatial correlation is effective. The removal of spectral correlation has limited capacity.Therefore, this article designs three-dimensional lossless compression algorithms for hyperspectral remote sensing images. HSI has hundreds of bands, which have different importance. It can be seen from the entropy of different bands. From the analysis, we can see that the particularity of HSI is the spectral dimensionality. The following compression methods will focus on the point. Thus the HSI compression research around the spectral feature will get better results.In the two-dimensional still image lossless prediction algorithm, now JPEG-LS is the best compression algorithm. Its core algorithm LOCO-I have a good performance about compression ratio and algorithm complexity. It is based on adaptive prediction, context modeling and Golomb coding method; two-dimensional image compression algorithm is not suitable for HIS. LOCO-I predictor will be extended to three dimensions, by removing the relevant, design a three-dimensional image compression predictor, Three-dimensional optimal combination predictor. Three-dimensional LCL-3D predictor. Context-based LOCO-3D Predictor, and using hybrid coding ideas for design suitable for hyperspectral image compression algorithm, compression algorithms have good effect in the compression ratio and complexity.