Adaptive parameter local consistency automatic outlier removal algorithm for area-based matching

Abstract

Due to the influence of image differences and matching methods, geometric calibration of remote sensing images often results in the extraction of control points with inevitable outliers. Moreover, it is susceptible to limitations imposed by locally constrained outlier rejection methods, making it challenging to automatically remove relatively small gross errors. This paper introduces an adaptive parameter local consistency automatic outlier removal algorithm, referred to as APLC. Initially, we construct k-nearest neighbors for each pair of matching points, deriving distance and topological uncertainty based on the accuracy of point matching. Subsequently, we conduct cross-validation on the uncertainty between the two pairs of vectors formed by points within the neighborhood, aiming for parameter adaptation. Finally, a cost-defined function is introduced to assess the consistency of local structures. Through a two-stage outlier removal strategy, matching points that do not maintain local structural consistency are eliminated. To assess the effectiveness of the proposed algorithm, we conduct experimental comparisons using region-based initial matching results from the FY-3D remote sensing dataset, demonstrating its superiority compared to three state-of-the-art methods.