针对合成孔径雷达(SAR)图像信噪比低且具有相干斑噪声,与光学图像配准时难度较大的现状,提出了一种异源图像村庄目标感兴趣区域(ROI)提取和匹配新方法。首先,对SAR 图像进行中值滤波和偏微分处理,消除噪声干扰;然后,利用灰度共生矩阵的四个纹理参数对整幅图进行计算,判断是否为村庄目标点,并提出局部灰度一致性算法,消除虚警点,增加目标点,完成村庄目标ROI 的粗提取;接着,利用傅里叶描述子对获得的目标ROI 进行二次描述和填充,获得最终待匹配的村庄ROI;最后,利用Hu 不变矩区域特征,结合最小欧氏距离测度和余弦相似度,实现异源图像匹配。实验结果表明,最终获得的配准图像误差较小,配准图像视觉判断比较理想,因而具有较高的可靠性和稳定性以及较高的理论研究价值和广泛的应用前景。

Since the signal noise ratio of synthetic aperture radar (SAR) image is low as well as speckle noise is much, it's quite difficult to match SAR image with optical image. A new method of target extraction and heterogeneous image matching is proposed in this paper. Median filter and partial differential method are firstly used to remove speckle noise. Next, the GLCM of whole image is calculated by four texture parameters to judge target or non-target points, and a new method which is called local gray-level congruency is proposed to clear false alarm points and increase target points at the same time. Then proper numbers of Fourier descriptor are used to describe ROI for matching and the ROI is filled as closed areas and is marked for final matching. Finally, Hu moment invariants, combined with Euclidean distance and cosine similarity are utilized to complete heterogonous image registration. The experimental results show that the RMSE of final registration is small, the heterogeneous registration image is accurate judged by sight and the whole method owns high reliability, stability and broad application prospect as well.

TN957.52