Distributed Polarimetric SAR Compression with Joint Deblocking Using Side Information

Quad-pol Synthetic Aperture Radar (SAR) images consist of four polarization schemes(HH, HV, VH, and VV), each having different responses from different types of terrain, foliage, and buildings. These polarimetric SAR images show correlations between cross-polarizations (HV and VH) and co-polarizations (HH and VV). Real-time transmission or storage of these large raw SAR images from UAVs in hostile electromagnetic environments becomes impractical due to file size, bandwidth constraints, and limited bit rate. To overcome this challenge, we propose a Distributed Polarimetric SAR Compression and Deblocking network (DPCD) that can exploit polarimetric correlations to improve compression efficiency. Our approach enables distributed and independent encoding of quad-pol SAR images while utilizing a learning-based method for joint frequency and spatial domain deblocking of the primary polarization, integrating feature-level information from Side Information (SI). Experimental results on the NGA SAR dataset show that DPCD outperforms state-of-the-art single-polarization compression techniques, particularly at lower bitrates, delivering significant bitrate savings without compromising image quality.