FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2752605280> ?p ?o ?g. }

• W2752605280 endingPage "58" @default.
• W2752605280 startingPage "50" @default.
• W2752605280 abstract "This study evaluates the use of automated and manual feature selection – prior to machine learning – for the differentiation of crops in a Mediterranean climate (Western Cape, South Africa). Five Landsat-8 images covering the different crop class phenological stages were acquired and used to generate a range of spectral and textural features within an object-based image analysis (OBIA) paradigm. The features were used as input to decision trees (DTs), k-nearest neighbour (k-NN), support vector machine (SVM), and random forest (RF) supervised classifiers. Testing was done by performing classifications (using all spatial variables) and then incrementally reducing the feature counts (based on importance allocated to features by filters), feature extraction, and manual (semantic) feature selection. Classification and regression trees (CART) and RF were used as methods to filter feature selection. Feature-extraction methods employed include principal components analysis (PCA) and Tasselled cap transformation (TCT). The classification results were analysed by comparing the overall accuracies and kappa coefficients of each scenario, while McNemar’s test was used to assess the statistical significance of differences in accuracies among classifiers. Feature selection was found to improve the overall accuracies of the DT, k-NN, and RF classifications, but reduced the accuracy of SVM. The results showed that SVM with feature extraction (PCA) on individual image dates produced the most accurate classification (96.2%). Semantic groupings of features for classification also revealed that using the image bands and indices is not sufficient for crop classification, and that additional features are needed. The accuracy differences of the classifiers were, however, not statistically significant, which suggests that, although dimensionality reduction can improve crop differentiation when multi-temporal Landsat-8 imagery is used, it had a marginal effect on the results. For operational crop-type classification in the study area (and similar regions), we conclude that the SVM algorithm can be applied to the full set of features generated." @default.
• W2752605280 created "2017-09-15" @default.
• W2752605280 creator A5052997943 @default.
• W2752605280 creator A5058702532 @default.
• W2752605280 date "2017-11-01" @default.
• W2752605280 modified "2023-10-16" @default.
• W2752605280 title "Value of dimensionality reduction for crop differentiation with multi-temporal imagery and machine learning" @default.
• W2752605280 cites W1885078460 @default.
• W2752605280 cites W1949967829 @default.
• W2752605280 cites W1964262728 @default.
• W2752605280 cites W1967400946 @default.
• W2752605280 cites W1967422307 @default.
• W2752605280 cites W1984792953 @default.
• W2752605280 cites W1986738039 @default.
• W2752605280 cites W1995736008 @default.
• W2752605280 cites W1998361807 @default.
• W2752605280 cites W2000179198 @default.
• W2752605280 cites W2014555541 @default.
• W2752605280 cites W2026934896 @default.
• W2752605280 cites W2043962305 @default.
• W2752605280 cites W2044364680 @default.
• W2752605280 cites W2063545471 @default.
• W2752605280 cites W2077519572 @default.
• W2752605280 cites W2082081125 @default.
• W2752605280 cites W2082874195 @default.
• W2752605280 cites W2084382156 @default.
• W2752605280 cites W2099507093 @default.
• W2752605280 cites W2101051003 @default.
• W2752605280 cites W2119387367 @default.
• W2752605280 cites W2127227873 @default.
• W2752605280 cites W2153635508 @default.
• W2752605280 cites W2155632266 @default.
• W2752605280 cites W2158864811 @default.
• W2752605280 cites W2166659908 @default.
• W2752605280 cites W2168809519 @default.
• W2752605280 cites W2311217941 @default.
• W2752605280 cites W2532592089 @default.
• W2752605280 cites W2585014453 @default.
• W2752605280 cites W2587456632 @default.
• W2752605280 doi "https://doi.org/10.1016/j.compag.2017.08.024" @default.
• W2752605280 hasPublicationYear "2017" @default.
• W2752605280 type Work @default.
• W2752605280 sameAs 2752605280 @default.
• W2752605280 citedByCount "38" @default.
• W2752605280 countsByYear W27526052802018 @default.
• W2752605280 countsByYear W27526052802019 @default.
• W2752605280 countsByYear W27526052802020 @default.
• W2752605280 countsByYear W27526052802021 @default.
• W2752605280 countsByYear W27526052802022 @default.
• W2752605280 countsByYear W27526052802023 @default.
• W2752605280 crossrefType "journal-article" @default.
• W2752605280 hasAuthorship W2752605280A5052997943 @default.
• W2752605280 hasAuthorship W2752605280A5058702532 @default.
• W2752605280 hasBestOaLocation W27526052801 @default.
• W2752605280 hasConcept C119857082 @default.
• W2752605280 hasConcept C12267149 @default.
• W2752605280 hasConcept C138885662 @default.
• W2752605280 hasConcept C148483581 @default.
• W2752605280 hasConcept C153180895 @default.
• W2752605280 hasConcept C154945302 @default.
• W2752605280 hasConcept C169258074 @default.
• W2752605280 hasConcept C2776401178 @default.
• W2752605280 hasConcept C33923547 @default.
• W2752605280 hasConcept C41008148 @default.
• W2752605280 hasConcept C41895202 @default.
• W2752605280 hasConcept C52622490 @default.
• W2752605280 hasConcept C70518039 @default.
• W2752605280 hasConcept C84525736 @default.
• W2752605280 hasConceptScore W2752605280C119857082 @default.
• W2752605280 hasConceptScore W2752605280C12267149 @default.
• W2752605280 hasConceptScore W2752605280C138885662 @default.
• W2752605280 hasConceptScore W2752605280C148483581 @default.
• W2752605280 hasConceptScore W2752605280C153180895 @default.
• W2752605280 hasConceptScore W2752605280C154945302 @default.
• W2752605280 hasConceptScore W2752605280C169258074 @default.
• W2752605280 hasConceptScore W2752605280C2776401178 @default.
• W2752605280 hasConceptScore W2752605280C33923547 @default.
• W2752605280 hasConceptScore W2752605280C41008148 @default.
• W2752605280 hasConceptScore W2752605280C41895202 @default.
• W2752605280 hasConceptScore W2752605280C52622490 @default.
• W2752605280 hasConceptScore W2752605280C70518039 @default.
• W2752605280 hasConceptScore W2752605280C84525736 @default.
• W2752605280 hasLocation W27526052801 @default.
• W2752605280 hasOpenAccess W2752605280 @default.
• W2752605280 hasPrimaryLocation W27526052801 @default.
• W2752605280 hasRelatedWork W1517228774 @default.
• W2752605280 hasRelatedWork W20047544 @default.
• W2752605280 hasRelatedWork W2117019857 @default.
• W2752605280 hasRelatedWork W2389704471 @default.
• W2752605280 hasRelatedWork W2767419625 @default.
• W2752605280 hasRelatedWork W3165907317 @default.
• W2752605280 hasRelatedWork W4313289487 @default.
• W2752605280 hasRelatedWork W4317732970 @default.
• W2752605280 hasRelatedWork W4323294312 @default.
• W2752605280 hasRelatedWork W4366990902 @default.
• W2752605280 hasVolume "142" @default.
• W2752605280 isParatext "false" @default.
• W2752605280 isRetracted "false" @default.

• next