| Abstrakt: |
Point feature-based (PFB) remote sensing image registration methods have achieved great accomplishments. However, such methods based on the hand-crafted point features (HCPFs) have encountered limitations of robustness, accuracy, efficiency, and automation for high-resolution remote sensing (HRRS) image registration. A two-stage registration framework specialized for HRRS images, which combines object and point features is introduced in this article. Specifically, in the first stage, we propose to abstract and represent the macroscopic semantic objects in HRRS images as the deep convolutional object features (DCOFs), which is used to implement the global coarse registration. In the second stage, specific local areas are selected according to the DCOF matching, and the PFB method is performed in the local areas to achieve refinement. The salient points of this proposal include that, 1) the proposed DCOF is more discriminative and expressive than the HCPF at the macroscopic scale so as to effectively achieve automatic and robust global preregistration. 2) We implement the object feature extraction in an object detection fashion which is more-or-less plug and play. 3) Our designed two-stage strategy plays to the advantages of the DCOF and HCPF in different scenarios, thereby improving accuracy and efficiency. Finally, experiments demonstrate the effectiveness of our method. Compared with the state-of-the-art PFB methods, it improves the accuracy by about 3$\%$–6$\%$ and the efficiency by about 15$\%$–30$\%$. [ABSTRACT FROM AUTHOR] |
