Robust Feature Matching via Hierarchical Local Structure Visualization

Feature matching, which refers to seeking good correspondence between two feature point sets, is a critical prerequisite in many applications of remote sensing and photogrammetry. This work can be viewed as an extension of LSV-ANet. Traditional local structure visualization (LSV) descriptor is very sensitive to outliers existing in the small region around a feature point, which limits the ability of LSV-ANet to recognize fine-grained patterns and its generalizability for complex matching scenes. Thus, in this letter, we propose a hierarchical local structure visualization (HLSV) to solve this issue. Specifically, local structures of feature points are first decoupled by hierarchical tensor and then reconstructed in a learning fashion, which explicitly permits us to learn a nonmutually exclusive relationship for enhancement structure manipulation. In addition, in order to further improve the network generalization ability, we design a permutation-invariant network layer to ensure that the output of model is invariant to the input order. To demonstrate the robustness of the HLSV, extensive experiments on various real remote sensing image pairs for feature matching are conducted. The experiment results reveal that HLSV is superior to the current eight state-of-the-art alternatives.