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Accuracy Assessment for Soft Classification Maps

dc.book.titleRemote Sensing of Natural Resources
dc.contributor.authorGómez González, Daniel
dc.contributor.authorBiging, Greg
dc.contributor.authorMontero De Juan, Francisco Javier
dc.contributor.editorGuangxing, Wang
dc.contributor.editorQihao, Weng
dc.date.accessioned2023-06-19T15:54:28Z
dc.date.available2023-06-19T15:54:28Z
dc.date.issued2013
dc.description.abstractAn important topic in using maps derived from a statistical classifier is the accuracy assessment of the classification. Analysts usually need to compare various techniques, algorithms, or different approaches. As pointed out by Stehman and Czaplewski (1998), the accuracy assessment of classification maps generally involves three different steps: the sampling design, the response or measurement design to obtain the true classes for each sampling (usually requiring an expert), and the analysis of the data obtained. In the ...en
dc.description.departmentDepto. de Estadística e Investigación Operativa
dc.description.facultyFac. de Ciencias Matemáticas
dc.description.refereedTRUE
dc.description.statuspub
dc.eprint.idhttps://eprints.ucm.es/id/eprint/28566
dc.identifier.isbn9781466556928
dc.identifier.relatedurlhttp://www.crcpress.com
dc.identifier.urihttps://hdl.handle.net/20.500.14352/35718
dc.issue.number57
dc.language.isoeng
dc.page.final66
dc.page.initial57
dc.page.total580
dc.publication.placeUSA
dc.publisherCRC Press
dc.relation.ispartofseriesRemote Sensing Applications Series
dc.rights.accessRightsrestricted access
dc.subject.cdu519.22
dc.subject.ucmEstadística matemática (Matemáticas)
dc.subject.unesco1209 Estadística
dc.titleAccuracy Assessment for Soft Classification Mapsen
dc.typebook part
dspace.entity.typePublication
relation.isAuthorOfPublication4dcf8c54-8545-4232-8acf-c163330fd0fe
relation.isAuthorOfPublication9e4cf7df-686c-452d-a98e-7b2602e9e0ea
relation.isAuthorOfPublication.latestForDiscovery4dcf8c54-8545-4232-8acf-c163330fd0fe

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