结合SIFT和Delaunay三角网的遥感图像配准算法

针对高分辨率遥感图像中提取的特征点数目过大且易存在误匹配点的问题，提出了一种粗配准和精配准相结合的高分辨率遥感图像配准算法.首先对图像降采样处理后，提取大尺度空间下的SIFT特征点，求得仿射变换模型完成图像粗配准；然后对图像进行分块，利用SIFT方法对每幅子块图像提取特征点，并找到对应子块图像之间的匹配点对；之后利用特征点构建Delaunay三角网，计算每对子块图像之间的三角形相似度，构成相似矩阵，从中挑选相似度大的三角形对以构成精确匹配点对；最后利用得到的精确匹配点对实现最终的图像配准.该算法能够减少提取的特征点数且剔除更多的错误匹配点，从而进一步提高精确匹配点率.实验结果表明了算法的有效性.

Remote Sensing Images Registration Algorithm Combining with SIFT and Delaunay Triangulation

Aiming at the problems such as false matching points and large volume remote sensing image registration, a high-resolution remote sensing images registration algorithm based on coarse and fine registration is proposed. Firstly, the high scale space feature points were extracted after down sampling the images to execute the coarse registration. Secondly, the initial set of feature points was extracted using the Scale Invariant Feature Transform (SIFT) algorithm for each block after using image blocking strategy. Furthermore, feature points were used to obtain the Delaunay triangulation, and then calculated the similarity between blocks of both images to select pairs of triangles which the similarity greater than threshold. Finally, the fine registration was achieved by precise feature points. The proposed algorithm can reduce the number of feature points, and it can eliminate more false matching points to increase the correct feature point matching rate. The experimental results indicate that the proposed method is effective.