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动态特征是海洋信息中的重要方面。但是在通常的SAR图像处理中动态信息往往会被丢失,因为这些方法大多把SAR图像看成是观测区域的瞬时状态。实际上,我们可以从SAR子孔径序列图像中获取动态信息,因为我们知道SAR不同方位向孔径对应不同的成像瞬间。从序列图像中获取动态信息的一个关键步骤就是图像匹配。但是SAR子孔径图像的强噪声特性使得传统的图像匹配算法难以奏效。该文中,为了应对SAR子孔径图像中的噪声问题,我们提出了一种改进的相位相关法。仿真实验表明改进的算法在多数情况下都可以达到0.15像素以上的精度以及很好的噪声鲁棒性。分析表明,该方法可以适用于从中等分辨的机载SAR图像和高分辨的星载SAR图像中提取动态特征,速度提取精度可以达到0.15-0.3 m/s。该文将该方法用于一个实际的机载SAR图像的处理,反演的海面动态速度在0.05-0.5 m/s左右,这个速度范围符合海面上一般的流速范围。
Dynamic features are an important aspect of ocean information. However, dynamic information is often lost in typical SAR image processing because most of these methods regard SAR images as the transient state of the observed area. In fact, we can get the dynamic information from SAR subaperture sequence images because we know that the imaging azimuth with different azimuths and apertures corresponds to different imaging instants. A key step in getting dynamic information from sequence images is image matching. However, the strong noise characteristic of SAR sub-aperture images makes the traditional image matching algorithms difficult to work. In this paper, we propose an improved phase-correlation method to deal with the noise problem in SAR sub-aperture images. Simulation results show that the improved algorithm can achieve the precision of 0.15 pixels and the noise robustness in most cases. The analysis shows that the method can be applied to extract dynamic features from medium resolution SAR images and high resolution spaceborne SAR images, and the speed of extraction can reach 0.15-0.3 m / s. In this paper, the method is applied to the processing of an actual airborne SAR image. The retrieved sea surface dynamic velocity is about 0.05-0.5 m / s, which is in line with the general flow velocity range above sea level.