Image local feature extraction is extensively utilized in the field of photogrammetry where the spatial distribution of features is important in high-quality image matching, particularly in high-resolution unmanned aerial vehicle (UAV) image registration. Presently, the spatial distribution problems are considered in some local feature extraction methods, though these methods are designed for point descriptors. Line descriptors are more robust to repetitive patterns compared to point descriptors and have attracted extensive attention in recent years. Hence, a feature extraction method is designed in this paper for line descriptors based on the K-connected virtual line descriptors matching method. Using the four measures, the quality of local features is quantified, and a regular gridding strategy based on the quality of local features is applied in the feature selection procedure. The proposed feature extraction method was successfully applied to match various simulated and real UAV-based images. Based on the experimental results using real images, it is indicated that two evaluation criteria, namely the spatial distribution quality of features and the number of correct matches, are improved to at least 12% and 15%, respectively, for verifying the capability of the proposed method to enhance matching performance.

中文翻译：

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一种基于虚拟线描述符的无人机图像配准局部特征提取方法

图像局部特征提取广泛用于摄影测量领域，其中特征的空间分布在高质量图像匹配中很重要，特别是在高分辨率无人机 (UAV) 图像配准中。目前，一些局部特征提取方法考虑了空间分布问题，尽管这些方法是为点描述符设计的。与点描述符相比，线描述符对重复模式的鲁棒性更强，近年来引起了广泛关注。因此，本文设计了一种基于K-连通虚拟线描述符匹配方法的线描述符特征提取方法。使用四个度量，量化局部特征的质量，在特征选择过程中应用基于局部特征质量的规则网格化策略。所提出的特征提取方法已成功应用于匹配各种模拟和真实的无人机图像。基于真实图像的实验结果表明，特征的空间分布质量和正确匹配的数量这两个评价标准分别提高到至少12%和15%，以验证该算法的能力。提出的方法来提高匹配性能。