Matches in SemOpenAlex for { <https://semopenalex.org/work/W2754041738> ?p ?o ?g. }
Showing items 1 to 93 of
93
with 100 items per page.
- W2754041738 endingPage "892" @default.
- W2754041738 startingPage "887" @default.
- W2754041738 abstract "Abstract. As an intermediate step between raw remote sensing data and digital urban maps, remote sensing data classification has been a challenging and long-standing research problem in the community of remote sensing. In this work, an effective classification method is proposed for classifying high-resolution remote sensing data over urban areas. Starting from high resolution multi-spectral images and 3D geometry data, our method proceeds in three main stages: feature extraction, classification, and classified result refinement. First, we extract color, vegetation index and texture features from the multi-spectral image and compute the height, elevation texture and differential morphological profile (DMP) features from the 3D geometry data. Then in the classification stage, multiple random forest (RF) classifiers are trained separately, then combined to form a RF ensemble to estimate each sample’s category probabilities. Finally the probabilities along with the feature importance indicator outputted by RF ensemble are used to construct a fully connected conditional random field (FCCRF) graph model, by which the classification results are refined through mean-field based statistical inference. Experiments on the ISPRS Semantic Labeling Contest dataset show that our proposed 3-stage method achieves 86.9% overall accuracy on the test data." @default.
- W2754041738 created "2017-09-25" @default.
- W2754041738 creator A5036413372 @default.
- W2754041738 creator A5085657358 @default.
- W2754041738 date "2017-09-13" @default.
- W2754041738 modified "2023-09-30" @default.
- W2754041738 title "RANDOM-FOREST-ENSEMBLE-BASED CLASSIFICATION OF HIGH-RESOLUTION REMOTE SENSING IMAGES AND NDSM OVER URBAN AREAS" @default.
- W2754041738 cites W1497089125 @default.
- W2754041738 cites W1686810756 @default.
- W2754041738 cites W1909515874 @default.
- W2754041738 cites W1912954554 @default.
- W2754041738 cites W1975063189 @default.
- W2754041738 cites W1978879784 @default.
- W2754041738 cites W1994699846 @default.
- W2754041738 cites W2001859769 @default.
- W2754041738 cites W2022105078 @default.
- W2754041738 cites W2036389990 @default.
- W2754041738 cites W2074378519 @default.
- W2754041738 cites W2077264955 @default.
- W2754041738 cites W2103699041 @default.
- W2754041738 cites W2112803241 @default.
- W2754041738 cites W2114819256 @default.
- W2754041738 cites W2115451191 @default.
- W2754041738 cites W2123576693 @default.
- W2754041738 cites W2125408776 @default.
- W2754041738 cites W2163605009 @default.
- W2754041738 cites W2168809519 @default.
- W2754041738 cites W2172000360 @default.
- W2754041738 cites W2184116797 @default.
- W2754041738 cites W2221898772 @default.
- W2754041738 cites W2335639764 @default.
- W2754041738 cites W2395611524 @default.
- W2754041738 cites W2469938794 @default.
- W2754041738 cites W2488187315 @default.
- W2754041738 cites W2745423625 @default.
- W2754041738 cites W27675589 @default.
- W2754041738 cites W2886742956 @default.
- W2754041738 cites W2949650786 @default.
- W2754041738 cites W2963542991 @default.
- W2754041738 cites W2963563573 @default.
- W2754041738 cites W2964288706 @default.
- W2754041738 cites W3165110222 @default.
- W2754041738 doi "https://doi.org/10.5194/isprs-archives-xlii-2-w7-887-2017" @default.
- W2754041738 hasPublicationYear "2017" @default.
- W2754041738 type Work @default.
- W2754041738 sameAs 2754041738 @default.
- W2754041738 citedByCount "2" @default.
- W2754041738 countsByYear W27540417382022 @default.
- W2754041738 crossrefType "journal-article" @default.
- W2754041738 hasAuthorship W2754041738A5036413372 @default.
- W2754041738 hasAuthorship W2754041738A5085657358 @default.
- W2754041738 hasBestOaLocation W27540417381 @default.
- W2754041738 hasConcept C115961682 @default.
- W2754041738 hasConcept C153180895 @default.
- W2754041738 hasConcept C154945302 @default.
- W2754041738 hasConcept C169258074 @default.
- W2754041738 hasConcept C181843262 @default.
- W2754041738 hasConcept C205649164 @default.
- W2754041738 hasConcept C41008148 @default.
- W2754041738 hasConcept C52622490 @default.
- W2754041738 hasConcept C62649853 @default.
- W2754041738 hasConcept C75294576 @default.
- W2754041738 hasConceptScore W2754041738C115961682 @default.
- W2754041738 hasConceptScore W2754041738C153180895 @default.
- W2754041738 hasConceptScore W2754041738C154945302 @default.
- W2754041738 hasConceptScore W2754041738C169258074 @default.
- W2754041738 hasConceptScore W2754041738C181843262 @default.
- W2754041738 hasConceptScore W2754041738C205649164 @default.
- W2754041738 hasConceptScore W2754041738C41008148 @default.
- W2754041738 hasConceptScore W2754041738C52622490 @default.
- W2754041738 hasConceptScore W2754041738C62649853 @default.
- W2754041738 hasConceptScore W2754041738C75294576 @default.
- W2754041738 hasLocation W27540417381 @default.
- W2754041738 hasLocation W27540417382 @default.
- W2754041738 hasOpenAccess W2754041738 @default.
- W2754041738 hasPrimaryLocation W27540417381 @default.
- W2754041738 hasRelatedWork W2144059113 @default.
- W2754041738 hasRelatedWork W2146076056 @default.
- W2754041738 hasRelatedWork W2734081726 @default.
- W2754041738 hasRelatedWork W2738461075 @default.
- W2754041738 hasRelatedWork W2811390910 @default.
- W2754041738 hasRelatedWork W2940977206 @default.
- W2754041738 hasRelatedWork W2964383635 @default.
- W2754041738 hasRelatedWork W3003836766 @default.
- W2754041738 hasRelatedWork W3005023910 @default.
- W2754041738 hasRelatedWork W4225114501 @default.
- W2754041738 hasVolume "XLII-2/W7" @default.
- W2754041738 isParatext "false" @default.
- W2754041738 isRetracted "false" @default.
- W2754041738 magId "2754041738" @default.
- W2754041738 workType "article" @default.