Contour Matching Method for SAR Images Based on Salient Contour Features
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摘要: 在以星载SAR图像作为基准图、机载/弹载SAR图像作为实时图的匹配导航和精确制导研究中，传统基于点特征的匹配方法存在特征点数目过多, 误匹配率较高，容易受噪声及灰度变化影响等问题。该文提出一种基于显著轮廓特征的SAR图像“由粗到精”的匹配新方法。该方法在对SAR图像进行预处理的基础上，采用改进的模糊C均值聚类(FCM)的图像分割方法来提取闭合轮廓特征；采用归一化轮廓中心距离描述符进行双向匹配，获得强鲁棒性的粗匹配轮廓对；在粗匹配轮廓上采用改进的局部二值模式(LBP)算子得到精匹配结果。试验结果表明，该方法在图像旋转、空间变化以及噪声干扰较大的情况下，具有精确性高、鲁棒性强的优势，适宜遥感SAR图像匹配。Abstract: In the research of matching navigation and precision guidance using spaceborne SAR image as the reference image and airborne/missile SAR image as the real-time image, the traditional point feature-based matching method has too many feature points, high mismatch rate, and easy affected by problems such as noise and gray level changes. A new method for matching SAR remote sensing images from coarse to fine based on salient contour features is proposed. Based on the pre-processing of SAR images, an improved Fuzzy C-Means (FCM) clustering image segmentation is used to to extract closed contour features. Then, a normalized contour center distance descriptor is constructed for two-way matching to obtain the rough matching contours with strong robustness. Finally, the improved Local Binary Pattern (LBP) operator is employed on the rough matching contours to gain the fine matching result. The experimental results demonstrate the proposed method has the advantages of high accuracy and strong robustness in the case of image rotation, spatial variation and noise interference, and is suitable for remote sensing SAR image matching.
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表 1 匹配方法定量比较分析
方法 RMSE 时间(ms) 匹配数 正确匹配数 正确率(%) SURF算法 6.952 218 26 21 80.77 SIFT-OCT算法 6.914 287 13 11 84.62 SAR-SIFT算法 5.071 310 19 17 89.47 本文所提方法 4.785 210 11 10 90.91 
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