摘要
由于合成孔径雷达(SAR)原始数据的相关性很低,直接压缩原始数据是比较困难的。该文提出一种新算法,先对SAR原始数据作距离聚焦处理,使其在方位向具有较强的相关性,再沿方位向作矢量线性预测,并对预测残差序列作分块自适应量化。结合一组实测SAR原始数据,用3种算法分别进行了压缩和解压缩,并计算了数据域及图像域信噪比,给出了3种压缩算法所成的图像。实验表明,在相同比特率条件下,该文算法得到的数据域信噪比和图像域信噪比均比BAQ算法高。该文算法的计算量远小于有关文献给出的距离聚焦后的压缩方法。
It is difficult to directly compress Synthetic Aperture Radar （SAR） raw data for its low relativity. In this paper, a new algorithm is put forward. Range focusing is imposed to SAR raw data, which makes it have comparative high relativity, then a vector linear prediction is performed along the azimuth direction, and block adaptive quantization is used to the prediction error series. By using a real SAR raw data, compression and decompression are made respectively. The SQNR and SDNR are achieved. The images correspond to the three algorithms are gained. The experiments manifest that with same bit rate, SQNR and SDNR after using the algorithm proposed in this paper surpass that of BAQ. The calculation in this paper is far less than that of compression method after range focusing advised in corresponding reference.
出处
《电子与信息学报》
EI
CSCD
北大核心
2008年第4期921-924,共4页
Journal of Electronics & Information Technology
基金
航空科学基金(05D52027)资助课题
关键词
合成孔径雷达
距离聚焦
矢量线性预测
信噪比
块自适应量化
比特率
Synthetic Aperture Radar （SAR）
Range focusing
Vector linear prediction
Signal to Noise Ratio （SNR）
Block Adaptive Quantization （BAQ）
Bit rate
