RFVTM: A Recovery and Filtering Vertex Trichotomy Matching for Remote Sensing Image Registration

Reliable feature point matching is a vital yet challenging process in feature-based image registration. In this paper, a robust feature point matching algorithm, which is called recovery and filtering vertex trichotomy matching, is proposed to remove outliers and retain sufficient inliers for remote sensing images. A novel affine-invariant descriptor, which is called the vertex trichotomy descriptor, is proposed on the basis of that geometrical relations between any of vertices and lines are preserved after affine transformations, which is constructed by mapping each vertex into trichotomy sets. The outlier removals in vertex trichotomy matching (VTM) are implemented by iteratively comparing the disparity of the corresponding vertex trichotomy descriptors. Some inliers mistakenly validated by a large number of outliers are removed in VTM iterations, and several residual outliers that are close to the correct locations cannot be excluded with the same graph structures. Therefore, a recovery and filtering strategy is designed to recover some inliers based on identical vertex trichotomy descriptors and restricted transformation errors. Assisted with the additional recovered inliers, residual outliers can be also filtered out during the process of reaching identical graphs for the expanded vertex sets. Experimental results demonstrate the superior performance on precision and stability of this algorithm under various conditions, such as remote sensing images with large transformations, duplicated patterns, or inconsistent spectral content.