Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/9049
DC FieldValueLanguage
dc.contributor.authorPark, No-Wook-
dc.contributor.authorKyriakidis, Phaedon-
dc.contributor.authorHong, Suk-Young-
dc.contributor.otherΚυριακίδης, Φαίδων-
dc.date.accessioned2017-01-16T10:50:31Z-
dc.date.available2017-01-16T10:50:31Z-
dc.date.issued2016-04-11-
dc.identifier.citationRemote Sensing, 2016, vol. 8, no. 4, pp. 320en_US
dc.identifier.issn20724292-
dc.identifier.urihttps://hdl.handle.net/20.500.14279/9049-
dc.description.abstractTraditional classification accuracy assessments based on summary statistics from a confusion matrix furnish a global (location invariant) view of classification accuracy. To estimate the spatial distribution of classification accuracy, a geostatistical integration approach is presented in this paper. Indicator kriging with local means is combined with logistic regression to integrate an image-derived ambiguity index with classification accuracy values at reference data locations. As for the ambiguity measure, a novel discrimination capability index (DCI) is defined from per class posteriori probabilities and then calibrated via logistic regression to derive soft probabilities. Integration of indicator-coded reference data with soft probabilities is finally carried out for mapping classification accuracy. It is demonstrated via a case study involving classification of multi-temporal and multi-sensor SAR datasets, that the proposed approach can provide a map of locally-varying accuracy values, while respecting the overall accuracy derived from the confusion matrix. It can also highlight areas where the benefit of data fusion was significant. It is expected that the indicator approach presented in this paper could be a useful methodology for assessing the spatial quality of classification results in a probabilistic way.en_US
dc.formatpdfen_US
dc.language.isoenen_US
dc.relation.ispartofRemote Sensingen_US
dc.rights© Multidisciplinary Digital Publishing Instituteen_US
dc.subjectClassificationen_US
dc.subjectAccuracyen_US
dc.subjectIndicator krigingen_US
dc.subjectPosteriori probabilityen_US
dc.titleSpatial estimation of classification accuracy using indicator kriging with an image-derived ambiguity indexen_US
dc.typeArticleen_US
dc.doi10.3390/rs8040320en_US
dc.collaborationInha Universityen_US
dc.collaborationCyprus University of Technologyen_US
dc.collaborationNational Institute of Agricultural Sciencesen_US
dc.subject.categoryElectrical Engineering - Electronic Engineering - Information Engineeringen_US
dc.journalsOpen Accessen_US
dc.countrySouth Koreaen_US
dc.countryCyprusen_US
dc.subject.fieldEngineering and Technologyen_US
dc.publicationNon Peer Revieweden_US
dc.identifier.doi10.3390/rs8040320en_US
dc.relation.issue4en_US
dc.relation.volume8en_US
cut.common.academicyear2015-2016en_US
dc.identifier.spage320en_US
item.openairecristypehttp://purl.org/coar/resource_type/c_6501-
item.openairetypearticle-
item.cerifentitytypePublications-
item.grantfulltextopen-
item.languageiso639-1en-
item.fulltextWith Fulltext-
crisitem.journal.journalissn2072-4292-
crisitem.journal.publisherMDPI-
crisitem.author.deptDepartment of Civil Engineering and Geomatics-
crisitem.author.facultyFaculty of Engineering and Technology-
crisitem.author.orcid0000-0003-4222-8567-
crisitem.author.parentorgFaculty of Engineering and Technology-
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