Frame-Based Locality Preservation Matching for Images Involving Large-Scale Transformations

Feature matching refers to the establishment of reliable correspondence between two sets of local features, which is an essential approach in remote sensing applications such as image registration and mosaicking. In this paper, a simple yet effective method, called frame-based locality preservation matching, is proposed for robust remote sensing image matching. We primarily focus on those images pairs that involve large-scale geometric transformations (e.g., extreme zoom). The key idea of our approach is to dig up the frame knowledge, such as the feature orientation and scale implied by common features like SIFT. The frame knowledge is free to obtain, and we find it to be of great significance in feature matching, especially for our focus -- large-scale geometric transformations. The proposed method can easily handle the geometric challenges and high outlier proportions, and significantly improves the performance compared to other state-of-the-art methods.