End-to-End Compression of Complex-Valued SAR Images

Compression of Synthetic Aperture Radar (SAR) images presents significant challenges due to inherent noise in the data acquisition process. SAR images consist of two components: “Amplitude” and “Phase.” The amplitude represents the magnitude of the radar signal, similar to the intensity of an optical image but with noise. Traditional SAR compression methods often apply existing optical image compression tools like JPEG, JPEG2000, etc. directly to the amplitude component. However, compressing the phase component is more problematic due to low information content, high noise level, and sensitivity to the quantization errors introduced by compression algorithms. A more effective approach involves transforming the amplitude and phase into representations that are easier to compress. Complex-valued SAR images achieve this by expressing the amplitude and phase as In-phase (I) and Quadrature-phase (Q) components. In this context, we propose an end-to-end deep learning pipeline specifically designed to compress complex-valued SAR images, ensuring that both amplitude and phase information are efficiently preserved and compressed. In addition, we applied frequency domain processing using subsampled 4×4 2D-DCT on complex-valued SAR images. This technique reduces the spatial resolution without information loss, effectively reducing the inference time on the CPU by almost twofold while maintaining acceptable performance metrics.