Lossless compression of hyperspectral imagery through 2D/3D hybrid prediction

Using the significant spectral correlation within the hyperspectral images, we present a lossless compression algorithm in this paper. By means of band ordering according to spectral correlation coefficient and 2D/3D hybrid prediction, which are based on local texture and neural networks, hyperspectral data are decorrelated. The prediction residuals are then entropy coded by context-based Golomb coding. Experimental results show that this method can remove the spatial and spectral redundancy efficiently and outperforms JPEG-ES and 3D-APA on average bit rate obviously.