Matches in SemOpenAlex for { <https://semopenalex.org/work/W2466601562> ?p ?o ?g. }
- W2466601562 endingPage "3610" @default.
- W2466601562 startingPage "3584" @default.
- W2466601562 abstract "This article introduces a method for road network extraction from satellite images. The proposed approach covers a new fusion method (using data from multiple sources) and a new Markov random field (MRF) defined on connected components along with a multilevel application (two-level MRF). Our method allows the detection of roads with different characteristics and decreases by around 30% the size of the used graph model. Results for synthetic aperture radar (SAR) images and optical images obtained using the TerraSAR-X and Quickbird sensors, respectively, are presented demonstrating the improvement brought by the proposed approach. In a second part, an analysis of different types of data fusion combining optical/radar images, radar/radar images, and multitemporal SAR (TerraSAR-X and COSMO-SkyMed) images is described. The qualitative and quantitative results show that the fusion approach improves considerably the results of the road network extraction." @default.
- W2466601562 created "2016-07-22" @default.
- W2466601562 creator A5006191190 @default.
- W2466601562 creator A5059988741 @default.
- W2466601562 creator A5077852561 @default.
- W2466601562 creator A5090955523 @default.
- W2466601562 date "2016-07-13" @default.
- W2466601562 modified "2023-10-01" @default.
- W2466601562 title "A two-level Markov random field for road network extraction and its application with optical, SAR, and multitemporal data" @default.
- W2466601562 cites W1549182744 @default.
- W2466601562 cites W1981329364 @default.
- W2466601562 cites W1981625538 @default.
- W2466601562 cites W2000666616 @default.
- W2466601562 cites W2003055616 @default.
- W2466601562 cites W2004225550 @default.
- W2466601562 cites W2007701819 @default.
- W2466601562 cites W2008162142 @default.
- W2466601562 cites W2015785930 @default.
- W2466601562 cites W2017069113 @default.
- W2466601562 cites W2024060531 @default.
- W2466601562 cites W2030660358 @default.
- W2466601562 cites W2030915124 @default.
- W2466601562 cites W2040356988 @default.
- W2466601562 cites W2050947934 @default.
- W2466601562 cites W2052647394 @default.
- W2466601562 cites W2055708609 @default.
- W2466601562 cites W2060276164 @default.
- W2466601562 cites W2080800401 @default.
- W2466601562 cites W2080892357 @default.
- W2466601562 cites W2081049226 @default.
- W2466601562 cites W2105833816 @default.
- W2466601562 cites W2109553965 @default.
- W2466601562 cites W2110317366 @default.
- W2466601562 cites W2113654450 @default.
- W2466601562 cites W2113979334 @default.
- W2466601562 cites W2120628612 @default.
- W2466601562 cites W2120700540 @default.
- W2466601562 cites W2133898791 @default.
- W2466601562 cites W2136997931 @default.
- W2466601562 cites W2141188882 @default.
- W2466601562 cites W2144494233 @default.
- W2466601562 cites W2144572173 @default.
- W2466601562 cites W2144615653 @default.
- W2466601562 cites W2145023731 @default.
- W2466601562 cites W2152866827 @default.
- W2466601562 cites W2156155065 @default.
- W2466601562 cites W2158449659 @default.
- W2466601562 cites W2161961148 @default.
- W2466601562 cites W2166130688 @default.
- W2466601562 cites W2166350183 @default.
- W2466601562 cites W2169553309 @default.
- W2466601562 cites W2170749316 @default.
- W2466601562 cites W2244429148 @default.
- W2466601562 cites W2307710359 @default.
- W2466601562 cites W2324183516 @default.
- W2466601562 cites W2563210919 @default.
- W2466601562 cites W2913455019 @default.
- W2466601562 cites W4200320221 @default.
- W2466601562 cites W4376596234 @default.
- W2466601562 doi "https://doi.org/10.1080/01431161.2016.1201227" @default.
- W2466601562 hasPublicationYear "2016" @default.
- W2466601562 type Work @default.
- W2466601562 sameAs 2466601562 @default.
- W2466601562 citedByCount "19" @default.
- W2466601562 countsByYear W24666015622017 @default.
- W2466601562 countsByYear W24666015622018 @default.
- W2466601562 countsByYear W24666015622019 @default.
- W2466601562 countsByYear W24666015622020 @default.
- W2466601562 countsByYear W24666015622021 @default.
- W2466601562 countsByYear W24666015622022 @default.
- W2466601562 countsByYear W24666015622023 @default.
- W2466601562 crossrefType "journal-article" @default.
- W2466601562 hasAuthorship W2466601562A5006191190 @default.
- W2466601562 hasAuthorship W2466601562A5059988741 @default.
- W2466601562 hasAuthorship W2466601562A5077852561 @default.
- W2466601562 hasAuthorship W2466601562A5090955523 @default.
- W2466601562 hasBestOaLocation W24666015622 @default.
- W2466601562 hasConcept C10929652 @default.
- W2466601562 hasConcept C115961682 @default.
- W2466601562 hasConcept C119857082 @default.
- W2466601562 hasConcept C124504099 @default.
- W2466601562 hasConcept C127313418 @default.
- W2466601562 hasConcept C127413603 @default.
- W2466601562 hasConcept C138885662 @default.
- W2466601562 hasConcept C146978453 @default.
- W2466601562 hasConcept C153180895 @default.
- W2466601562 hasConcept C154945302 @default.
- W2466601562 hasConcept C158525013 @default.
- W2466601562 hasConcept C19269812 @default.
- W2466601562 hasConcept C202444582 @default.
- W2466601562 hasConcept C2778045648 @default.
- W2466601562 hasConcept C31972630 @default.
- W2466601562 hasConcept C33923547 @default.
- W2466601562 hasConcept C33954974 @default.
- W2466601562 hasConcept C41008148 @default.
- W2466601562 hasConcept C41895202 @default.
- W2466601562 hasConcept C554190296 @default.
- W2466601562 hasConcept C62649853 @default.