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首页> 外文期刊>Geoscience and Remote Sensing Letters, IEEE >A Robust Delaunay Triangulation Matching for Multispectral/Multidate Remote Sensing Image Registration
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A Robust Delaunay Triangulation Matching for Multispectral/Multidate Remote Sensing Image Registration

机译：鲁棒的Delaunay三角剖分匹配用于多光谱/多日期遥感图像配准

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摘要

A novel dual-graph-based matching method is proposed in this letter particularly for the multispectral/multidate images with low overlapping areas, similar patterns, or large transformations. First, scale invariant feature transform based matching is improved by normalizing gradient orientations and maximizing the scale ratio similarity of all corresponding points. Next, Delaunay graphs are generated for outlier removal, and the candidate outliers are selected by comparing the distinction of Delaunay graph structures. In order to bring back the inliers removed in Delaunay triangulation matching iterations and to exclude the remaining outliers, the recovery strategy equipped with the dual graph of Delaunay is explored. Inliers located in the corresponding Voronoi cells are recovered to the residual sets. The experimental results demonstrate the accuracy and robustness of the proposed algorithm for various representative remote sensing images.
机译：在这封信中，提出了一种新颖的基于双图的匹配方法，特别是针对具有低重叠区域，相似图案或大变换的多光谱/多日期图像。首先，通过归一化梯度方向并最大化所有对应点的比例比例相似度，改进了基于比例不变特征变换的匹配。接下来，生成Delaunay图以除去异常值，并通过比较Delaunay图结构的区别来选择候选离群值。为了带回在Delaunay三角剖分匹配迭代中删除的内点并排除其余的离群值，研究了配备Delaunay对偶图的恢复策略。位于相应Voronoi单元中的Inliers被恢复到残差集。实验结果证明了该算法对各种代表性遥感图像的准确性和鲁棒性。

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