基于多尺度支撑域描述子的多光谱图像匹配算法

Multi-spectral image registration algorithm based on multi-scale support region descriptors

针对现有多光谱图像匹配算法鲁棒性不强的问题,提出一种新的基于多尺度支撑域描述子的多光谱图像匹配算法。该算法首先提取Harris角点作为特征点;然后分别统计特征点不同尺度邻域内的边缘方向直方图,组合构成特征描述子;以欧氏距离为相似度准则,使用比值法获得初始匹配结果;最后提出了一种基于RANSAC算法的外点去除算法。实验结果表明,该算法可有效匹配多光谱图像,且与已有算法相比鲁棒性更强,获取的正确匹配对更多。

To improve the robustness of the existing multi-spectral image registration algorithms,this paper proposed a new algorithm based on multi-scale support region descriptors.Firstly,the algorithm extracted the Harris corner points as feature points.Secondly,it constructed the descriptor by combining the edge direction histograms calculated respectively in the support regions of different sizes around a feature point.Then,the similarity criterion was the Euclidean distance,and it obtained the initial matches by the ratio method.Finally,this paper proposed an outlier removal algorithm based on RANSAC algorithm.The experimental results show that the proposed algorithm can match multi-spectral images effectively,be more robust than the existing algorithms, and obtains more matches that are correct.