  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.
