Registration-Is-Evaluation: Robust Point Set Matching With Multigranular Prior Assessment

Abstract

Point set registration is one of the challenging tasks in remote sensing image processing and analysis. Its critical step is to find the corresponding relationships between the fixed scene point set and the moving model point set that undergo different sorts of transformations. Existing algorithms primarily utilize different types of prior information to improve the registration performance, such as spatial consistency, local similarity, and uniform outliers. However, due to the lack of active evaluation on the prior and intermediate information during the registration process, these strategies are susceptible to large data transformations. In order to enhance the robustness and accuracy for point set registration, we propose in this article a novel framework, namely Registration-is-Evaluation (RisE). Based on a multigranular probability model, our method exploits and utilizes both prior and posterior information to dynamically evaluate the matching status. What is more, instead of adding an extra uniform prior, we unified the outliers, noise, missing points, and heavily warped points into our registration evaluation model and address them simultaneously. We also apply a novel point set descriptor, called local polar relative geometry (LPRG), to have a more robust local similarity measurement. It adopts the local polar coordinate to perform multiscale pooling and relative geometric computation. Based on our proposed method, the matching relationships and the spatial transformations can be actively evaluated to provide useful contextual guidance for the registration process. Experimental results on multiple data sets show that our algorithm outperforms the state-of-the-art methods, in terms of both accuracy and robustness under large data degradations.