A New Orientation Estimation Method Based on Rotation Invariant Gradient for Feature Points
Wenhui Xu, Sheng Zhong, Weijun Zhang, Jianhui Wang, Luxin Yan · IEEE Geoscience and Remote Sensing Letters · 2020
For many remote sensing image applications, orientation estimation is a crucial step for feature points extraction and matching, but it has attracted little attention. Due to the intensity differences between remote sensing image pairs, it is difficult to estimate the orientations of corresponding points accurately, resulting in performance degradation of feature matching. Thus, encountering the intensity differences, a robust spatial structure description for feature regions, and an effective calculation manner from description to orientation play a key role in accurate orientation estimation. To this end, in this letter, we first define a plausible orientation for feature points by the total gradient, offering an effective way to convert the gradient trend of the feature region to orientation. Therefore, we further propose a novel orientation estimation method, in which the rotation invariant gradient is introduced to improve the accuracy of gradient calculation and robustness of spatial structure description. Experimental results on multisensor remote sensing images demonstrate that our method increases the orientation estimation accuracy remarkably and outperforms other orientation estimation methods by a large margin, and effectively improves the performance of feature matching.