Applying Distributed Source Coding Principles to Remote Sensing Image Compression

This paper deals with the application of distributed source coding (DSC) theory to remote sensing image processing systems. We first provide a brief overview of DSC of correlated information sources. We discuss several possible applications of DSC in a remote sensing system, aimed at improving the performance of on-board, on-the-ground and hybrid lossless image compression systems. We then focus on one specific application, namely on-board lossless image compression, and apply DSC techniques in order to reduce the complexity of the on-board encoder, at the expense of the decoder’s, by exploiting the correlation of different bands of a hyperspectral dataset. Specifically, we propose two different compression schemes, one based on powerful binary error-correcting codes employed as source codes, and one based on simpler multilevel coset codes. The performance of both schemes is evaluated on a few AVIRIS scenes, and compared with other state-of-the-art 2-D and 3-D compression schemes. Both schemes turn out to achieve very competitive compression performance, and one of them also has reduced complexity. Based on these results, we highlight the main technical issues that are still to be solved to further improve the performance of DSC-based remote sensing systems.