Research On SAR Raw Data Compression Algorithm Based On Imaging

Due to its strong ability of high resolution in imaging, all 24 hours a day and any weather in working mode, and multi-band and multi-polar in developing trend, Synthetic Aperture Radar (SAR) has been extensively applied to kinds of fields, both in public and by military. As one kind of Radar imaging technique developed eventually since the fifty's in last century, SAR facilities the life of human-beings and the national defense greatly in recent years, and definitely own a wider development prospect. In order to meet the growing needs of information, the amount of echo data increases rapidly, however, which is not tolerated by the physical bandwidth of download link. Therefore, SAR raw data compression technique emerges with the tide in ages.This paper commences with the basis principle of data compression, focuses on the quantization theory used in entropy compression. Subsequently, we discuss the SAR imaging simply and analyze the raw data statistic character. Based on which, we choose and achieve three classical SAR raw data compression algorithms, all compared and analyzed in raw data domain and image domain.There are three ways existing in SAR raw data compression field, scalar quantization, vector quantization and based-transform domain quantization. In this paper, we pick up three algorithms to represent our experiment, namely by Block Adaptive Quantization (BAQ), Amplitude-Phase (AP) and FFT-BAQ. The first two are performed in time domain and based on scalar quantization and the last one is performed in frequency domain. Among them, BAQ is the earliest and most primary algorithm, which aims to the real part and imagery part of 2-D complex raw data and be easy to apply on hardware. AP is very similar to BAQ in some extent except it aims at keeping the phase information. The experiment result presents that AP leading the ability of protecting phase information which will be beneficial for imaging later. FFT-BAQ consists of two BAQ and one operation of transform. This method makes advantage of data reconstructed by energy concentration after some transformation, so the lower frequency part can be quantized with multi-bit and the higher frequency part can be quantized with low-bit, namely the bit-rate variable compression. In the situation of the same compression ratio, FFT-BAQ can retain raw data better than BAQ and present high performance.