【摘要】 误匹配剔除是提高特征匹配精度的重要手段，星载光学遥感图像由于数据量大、纹理重复、光照强度变化等特点，导致现有误匹配剔除方法的性能下降。针对此问题，提出一种基于局部和全局几何约束的误匹配剔除方法。在初始匹配集的基础上，首先利用特征局部一致性对误匹配进行初步过滤；然后根据图像间的变换关系构建特征拓扑结构，并提取其几何属性描述结构相似度，基于此建立特征全局结构一致性约束模型，通过推导模型的最优解剔除残留误匹配；采用引导式匹配策略，选取局部一致性高的匹配点组成高内点率匹配集，以此作为特征全局邻域，提高全局约束的鲁棒性和效率。实验结果表明，与现有方法相比，所提方法对星载光学遥感图像的匹配性能更优，平均精确率、召回率分别为0.9和0.89；在不同内点率的初始匹配集上表现鲁棒，平均F分数为0.86。更多还原

【Abstract】 Mismatch elimination is an important means of improving the accuracy of feature matching. Due to the large amount of data, texture duplication, light intensity changes, and other characteristics of satellite-borne optical remote sensing images, the performance of existing mismatch elimination methods is degraded. To solve this problem, a method based on local and guided global geometric constraints is proposed to eliminate mismatches. Based on the initial matching set, local consistency of features is used to filter out mismatches. Then, according to the transformation relationship between images, a feature topological structure is constructed, and its geometric attributes are extracted to describe structural similarity. Based on this, a feature global structure consistency constraint model is established, and residual mismatches are eliminated by deriving the optimal solution of the model. A guided matching strategy is adopted for global constraint, and matching points with high local consistency are selected to form a high internal point rate matching set, which is applied as the feature global neighborhood to improve the robustness and efficiency of the algorithm. The experimental results show that, in comparison with existing methods, the proposed method has better matching performance for satellite-borne optical remote sensing images, with an average accuracy and recall of 0. 9 and 0. 89, respectively. It is robust on the initial matching set with different internal point rates, and the average F score is 0. 86.更多还原