Delaunay三角网优化下的小面元遥感影像配准算法

针对遥感影像配准中控制点分布不均匀而影响配准精度的问题，论文提出三角网优化模型下的小面元遥感影像配准算法。首先利用RFM模型与DSM数据对其进行正射纠正；其次采用SIFT算子匹配特征点，通过RANSAC算法对其优化；同时设置影像边缘格网点，综合利用仿射变换、核线约束和灰度相似性约束匹配边缘格网点；构建初始Delaunay三角网，通过三角单元面积与角度双重约束优化三角网；最终通过扫描线填充算法实现小面元影像配准。多组实验结果表明该算法在遥感影像配准中的适用性和有效性，影像配准精度可达到亚像素级，使得存在地形起伏的遥感影像配准问题得到了有效解决。 

A tiny facet primitive remote sensing image registration algorithm based on optimized Delaunay Triangulation

To solve the accuracy problem of remote sensing image registration which is influenced by the uneven distribution of control points, a tiny facet primitive remote sensing image registration algorithm based on the optimized Delaunay Triangulation is proposed in this paper. Firstly, the Rational Function Model(RFM) and Digital Surface Model(DSM) are used for image ortho rectification. Secondly, feature points are matched through the SIFT operator, and optimized by the RANSAC algorithm. At the same time, the edge grid points are extracted, and the affine transformation, epipolar constraint and gray similarity constraint are used to match them. Then the initial Delaunay Triangulation is built, and based on the triangle area constraint and triangle angle optimized that. Finally, the tiny facet primitive image registration is achieved through the scan line filling algorithm. In this paper, simultaneous and multi-temporal remote sensing images are taken as data source to conduct the experiment. The experimental results of sets of data show that the applicability and validity, and accuracy of image registration can reach sub-pixel level, and effectively solves the problem which exists in remote sensing images with complicated geometric deformation.