English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Reliable feature point matching is a vital yet challenging process infeature-based image registration. In this paper,a robust feature point matchingalgorithm called Recovery and Filtering Vertex Trichotomy Matching (RFVTM) isproposed to remove outliers and retain sufficient inliers for remote sensingimages. A novel affine invariant descriptor called vertex trichotomy descriptoris proposed on the basis of that geometrical relations between any of verticesand lines are preserved after affine transformations, which is constructed bymapping each vertex into trichotomy sets. The outlier removals in VertexTrichotomy Matching (VTM) are implemented by iteratively comparing thedisparity of corresponding vertex trichotomy descriptors. Some inliersmistakenly validated by a large amount of outliers are removed in VTMiterations, and several residual outliers close to correct locations cannot beexcluded with the same graph structures. Therefore, a recovery and filteringstrategy is designed to recover some inliers based on identical vertextrichotomy descriptors and restricted transformation errors. Assisted with theadditional recovered inliers, residual outliers can also be filtered out duringthe process of reaching identical graph for the expanded vertex sets.Experimental results demonstrate the superior performance on precision andstability of this algorithm under various conditions, such as remote sensingimages with large transformations, duplicated patterns, or inconsistentspectral content.

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