Matches in SemOpenAlex for { <https://semopenalex.org/work/W3202023994> ?p ?o ?g. }
Showing items 1 to 75 of
75
with 100 items per page.
- W3202023994 endingPage "454" @default.
- W3202023994 startingPage "445" @default.
- W3202023994 abstract "AbstractThis paper focuses on the interpretation of the earth observation data using unsupervised machine learning for land cover classification. Synthetic aperture radar (SAR) data is considered as it has become prominent due to its nature of all-weather observation, cloud and dust penetration. For better accuracy and efficient training of a machine learning model, ground truth is important. In remotely sensed images, there are difficulties in obtaining the ground truth as the earth data are dependent on time, location of the region and position of the sensor capturing the image. To overcome the disadvantages due to the lack of ground truth, unsupervised clustering is used, which does not require any reference data. In this paper, partitioning clustering algorithm (K-Means) and model-based clustering algorithm (Expectation Maximization estimation) are applied and analysed for land cover classification in SAR data. On comparing the algorithms, the K-means performs better than the EM clustering.KeywordsLand cover classificationUnsupervised clusteringMachine learningRemote sensingSynthetic aperture radar" @default.
- W3202023994 created "2021-10-11" @default.
- W3202023994 creator A5017366966 @default.
- W3202023994 creator A5056331864 @default.
- W3202023994 date "2021-01-01" @default.
- W3202023994 modified "2023-10-16" @default.
- W3202023994 title "Unsupervised Land Cover Classification on SAR Images by Clustering Backscatter Coefficients" @default.
- W3202023994 cites W2010610937 @default.
- W3202023994 cites W2021629406 @default.
- W3202023994 cites W2030391296 @default.
- W3202023994 cites W2053381674 @default.
- W3202023994 cites W2113460284 @default.
- W3202023994 cites W2113586398 @default.
- W3202023994 cites W2126806605 @default.
- W3202023994 cites W2147169357 @default.
- W3202023994 cites W2218047931 @default.
- W3202023994 cites W2532171111 @default.
- W3202023994 cites W2897236511 @default.
- W3202023994 cites W2952411786 @default.
- W3202023994 cites W4245199042 @default.
- W3202023994 cites W88334678 @default.
- W3202023994 cites W2976971678 @default.
- W3202023994 doi "https://doi.org/10.1007/978-981-16-2248-9_43" @default.
- W3202023994 hasPublicationYear "2021" @default.
- W3202023994 type Work @default.
- W3202023994 sameAs 3202023994 @default.
- W3202023994 citedByCount "0" @default.
- W3202023994 crossrefType "book-chapter" @default.
- W3202023994 hasAuthorship W3202023994A5017366966 @default.
- W3202023994 hasAuthorship W3202023994A5056331864 @default.
- W3202023994 hasConcept C124101348 @default.
- W3202023994 hasConcept C127413603 @default.
- W3202023994 hasConcept C146849305 @default.
- W3202023994 hasConcept C147176958 @default.
- W3202023994 hasConcept C153180895 @default.
- W3202023994 hasConcept C154945302 @default.
- W3202023994 hasConcept C205649164 @default.
- W3202023994 hasConcept C2780648208 @default.
- W3202023994 hasConcept C41008148 @default.
- W3202023994 hasConcept C4792198 @default.
- W3202023994 hasConcept C62649853 @default.
- W3202023994 hasConcept C73555534 @default.
- W3202023994 hasConcept C87360688 @default.
- W3202023994 hasConceptScore W3202023994C124101348 @default.
- W3202023994 hasConceptScore W3202023994C127413603 @default.
- W3202023994 hasConceptScore W3202023994C146849305 @default.
- W3202023994 hasConceptScore W3202023994C147176958 @default.
- W3202023994 hasConceptScore W3202023994C153180895 @default.
- W3202023994 hasConceptScore W3202023994C154945302 @default.
- W3202023994 hasConceptScore W3202023994C205649164 @default.
- W3202023994 hasConceptScore W3202023994C2780648208 @default.
- W3202023994 hasConceptScore W3202023994C41008148 @default.
- W3202023994 hasConceptScore W3202023994C4792198 @default.
- W3202023994 hasConceptScore W3202023994C62649853 @default.
- W3202023994 hasConceptScore W3202023994C73555534 @default.
- W3202023994 hasConceptScore W3202023994C87360688 @default.
- W3202023994 hasLocation W32020239941 @default.
- W3202023994 hasOpenAccess W3202023994 @default.
- W3202023994 hasPrimaryLocation W32020239941 @default.
- W3202023994 hasRelatedWork W1964120219 @default.
- W3202023994 hasRelatedWork W2016291450 @default.
- W3202023994 hasRelatedWork W2062264607 @default.
- W3202023994 hasRelatedWork W2152091220 @default.
- W3202023994 hasRelatedWork W2381228848 @default.
- W3202023994 hasRelatedWork W2408718570 @default.
- W3202023994 hasRelatedWork W2543161807 @default.
- W3202023994 hasRelatedWork W2973417394 @default.
- W3202023994 hasRelatedWork W3090288395 @default.
- W3202023994 hasRelatedWork W4220723725 @default.
- W3202023994 isParatext "false" @default.
- W3202023994 isRetracted "false" @default.
- W3202023994 magId "3202023994" @default.
- W3202023994 workType "book-chapter" @default.