Ultra-wide Baseline Aerial Imagery Matching in Urban Environments
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Summary
This paper proposes a method for correspondence estimation that addresses this challenge for aerial scenes in urban environments using a RANSAC-based approach aided by self-similarity graph-based sampling and demonstrates improved performance to alternative methods in the literature.
- Type
- article
- Published
- 2013-01-01
- Cited by
- 6
- References
- 36
- OpenAlex
- https://openalex.org/W1975417987
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:1398767
Keywords
Baseline (sea), Aerial imagery, Computer science, Matching (statistics), Remote sensing
References
- Multiple View Geometry in Computer Vision
- Geometric invariance in computer vision
- Detecting planar homographies in an image pair
- Generalized RANSAC Framework for Relaxed Correspondence Problems
- Robust feature point matching by preserving local geometric consistency
- D-Nets: Beyond patch-based image descriptors
- FREAK: Fast Retina Keypoint
- Wide Baseline Stereo Matching based on Local, Affinely Invariant Regions
- Robust Feature Matching Through Maximal Cliques
- Recognition of spatial point patterns
- Image retrieval with geometry-preserving visual phrases
- ASIFT: An Algorithm for Fully Affine Invariant Comparison
- Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography
- Building recognition using sketch-based representations and spectral graph matching
- Point pattern matching with robust spectral correspondence
- A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
- Matching with PROSAC - progressive sample consensus
- A Combined Corner and Edge Detector
- Object recognition from local scale-invariant features
- Robust wide-baseline stereo from maximally stable extremal regions
Cited by
- MODS: Fast and robust method for two-view matching
- Learning to Match Aerial Images with Deep Attentive Architectures
- Regularity-Driven Building Facade Matching between Aerial and Street Views
- Multi-View Instance Matching with Learned Geometric Soft-Constraints
- A new productive framework for point‐based matching of oblique aircraft and UAV‐based images
- Hierarchical image geo-location on a world-wide scale
- RepMatch: Robust Feature Matching and Pose for Reconstructing Modern Cities
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