SAR raw data compression based on geometric characteristic of Gaussian curve
Juanni Liu, Quan Fa Zhou · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2015
Because of simple and good performance, the block adaptive quantization (BAQ) algorithm becomes a popular method for spaceborne synthetic aperture radar (SAR) raw data compression. As the distribution of SAR data can be accurately modeled as Gaussian, the algorithm adaptively quantizes the SAR data using Llyod-Max quantizer, which is optimal for standard Gaussian signal. However, due to the complexity of the imaging target features, the probability distribution function of some SAR data deviates from the Gaussian distribution, so the BAQ compression performance declined. In view of this situation, this paper proposes a method to judge whether the data satisfies Gaussian distribution by using the geometrical relationship between standard Gaussian curve and a triangle whose area is equal to that of the Gaussian curve, then getting the coordinates of the intersection of two curves, and comparing the integral value within each node to form three judgment conditions. Finally, the data satisfying these conditions is compressed by BAQ, otherwise compressed by DPCM. Experimental results indicate that the proposed scheme improves the performance compared with BAQ method.