SAR图像最佳欧式空间距离矩阵匹配方法

在基于尺度不变特征变换算法的合成孔径雷达图像配准算法中,一个特征点通常具有多个主方向,虽然该主方向分配方式可以有效增加正确匹配对数,但是匹配性能会受到特征向量之间的相互影响而下降。文章提出了一种最佳欧式距离匹配方法,该方法通过欧式空间距离矩阵计算待匹配图像两组特征向量集的相似度,获得最佳相似特征点。此外,文章引入代表位置关系的转换距离作为判断特征点空间一致性的依据,有效地消除错误匹配点。与DM等匹配方法相比较,最佳欧式空间距离矩阵匹配方法在匹配精度和匹配效率上验证了其有效性。

Optimal Euclidean distance matrix matching method for synthetic aperture radar images

In the application of synthetic aperture radar (SAR) image registration based on the scale invariant feature transform (SIFT) algorithm, a keypoint is always assigned with several dominant orientations. Thought the number of matches is increased, the feature matching performance usually decreases significantly with the infuluence of the feature vectors extracted with different orientations. An optimal Euclidean distance matrix (OEDM) is proposed for two sets of feature vectors to enhance the matching performance. The most similar keypoints are selected from the OEDM. In addition, spatial consistency of the keypoints from the two images is maintained by calculating the transformed distances, and the incorrect matches are eliminated effectively. Comparison with traditional dual matching (DM) methods is performed. The experimental results demonstrate the superiorities of the proposed method in both accuracy and efficiency.