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Road networks as collections of minimum cost paths.
- Source :
-
ISPRS Journal of Photogrammetry & Remote Sensing . Oct2015, Vol. 108, p128-137. 10p. - Publication Year :
- 2015
-
Abstract
- We present a probabilistic representation of network structures in images. Our target application is the extraction of urban roads from aerial images. Roads appear as thin, elongated, partially curved structures forming a loopy graph, and this complex layout requires a prior that goes beyond standard smoothness and co-occurrence assumptions. In the proposed model the network is represented as a union of 1D paths connecting distant (super-)pixels. A large set of putative candidate paths is constructed in such a way that they include the true network as much as possible, by searching for minimum cost paths in the foreground ( road ) likelihood. Selecting the optimal subset of candidate paths is posed as MAP inference in a higher-order conditional random field. Each path forms a higher-order clique with a type of clique potential, which attracts the member nodes of cliques with high cumulative road evidence to the foreground label. That formulation induces a robust P N -Potts model, for which a global MAP solution can be found efficiently with graph cuts. Experiments with two road data sets show that the proposed model significantly improves per-pixel accuracies as well as the overall topological network quality with respect to several baselines. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 09242716
- Volume :
- 108
- Database :
- Academic Search Index
- Journal :
- ISPRS Journal of Photogrammetry & Remote Sensing
- Publication Type :
- Academic Journal
- Accession number :
- 109981126
- Full Text :
- https://doi.org/10.1016/j.isprsjprs.2015.07.002