RFVTM: A Recovery and Filtering Vertex Trichotomy Matching for Remote Sensing Image Registration
This work addresses a critical challenge in remote sensing image registration, offering a robust solution for applications in fields like geospatial analysis, though it appears incremental as it builds on existing matching methods.
The authors tackled the problem of reliable feature point matching for remote sensing image registration by proposing RFVTM, which uses a novel affine invariant descriptor and a recovery-filtering strategy to remove outliers and retain inliers, achieving superior precision and stability under various conditions like large transformations and duplicated patterns.
Reliable feature point matching is a vital yet challenging process in feature-based image registration. In this paper,a robust feature point matching algorithm called Recovery and Filtering Vertex Trichotomy Matching (RFVTM) is proposed to remove outliers and retain sufficient inliers for remote sensing images. A novel affine invariant descriptor called 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 corresponding vertex trichotomy descriptors. Some inliers mistakenly validated by a large amount of outliers are removed in VTM iterations, and several residual outliers close to 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 also be filtered out during the process of reaching identical graph 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.