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Identification of low accuracy regions in land cover maps using uncertainty measures and classification confidence

2018-09-19Documento de conferência Acesso aberto
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dc.contributor.authorFonte, Cidália C.
dc.contributor.authorGonçalves, Luísa M. S.
dc.contributor.institutionNOVA Information Management School (NOVA IMS)
dc.date.accessioned2018-11-21T23:07:12Z
dc.date.available2018-11-21T23:07:12Z
dc.date.issued2018-09-19
dc.descriptionFonte, C. C., & Gonçalves, L. M. S. (2018). Identification of low accuracy regions in land cover maps using uncertainty measures and classification confidence. In SPRS TC IV Mid-term Symposium “3D Spatial Information Science – The Engine of Change” (4 ed., Vol. 42, pp. 275-281). (International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives). DOI: 10.5194/isprs-archives-XLII-4-201-2018
dc.description.abstractThe aim of this article is to assess if the data provided by soft classifiers and uncertainty measures can be used to identify regions with different levels of accuracy in a classified image. To this aim a soft Bayesian classifier was used, which enables the assignment of classifications confidence levels to all pixels. Two uncertainty measures were also used, namely the Relative Maximum Deviation (RMD) uncertainty measure and the Normalized Entropy (NE). The approach was tested on a case study. A multispectral IKONOS image was classified and the classification uncertainty and confidence where computed and analysed. Regions with different levels of uncertainty and confidence were identified. Reference datasets were then used to assess the classification accuracy of the whole study area and also in the regions with different levels of uncertainty and confidence. A comparative analysis was made on the variation of accuracy and classification uncertainty and confidence along the map and per class. The results show that for the regions with more uncertainty or less confidence the spatially constrained confusion matrices always generate lower values of global accuracy than for global accuracy of the regions with less uncertainty or more confidence. The analysis of the user’s and producer’s accuracy also shows the same general tendency. Proposals are then made on methodologies to use the information provided by the uncertainty and confidence to identify less reliable regions and also to improve classification results using fully automated approaches.en
dc.description.versionpublishersversion
dc.description.versionpublished
dc.format.extent7
dc.format.extent4653420
dc.identifier.doi10.5194/isprs-archives-XLII-4-201-2018
dc.identifier.issn1682-1750
dc.identifier.otherPURE: 6428104
dc.identifier.otherPURE UUID: 18c2d727-d3fa-4d69-9147-7c5db439e42e
dc.identifier.otherScopus: 85056208772
dc.identifier.urihttp://www.scopus.com/inward/record.url?scp=85056208772&partnerID=8YFLogxK
dc.identifier.urlhttps://www.scopus.com/pages/publications/85056208772
dc.language.isoeng
dc.peerreviewedyes
dc.subjectAccuracy
dc.subjectClassification
dc.subjectConfidence
dc.subjectMultispectral images
dc.subjectSpatial variation
dc.subjectUncertainty
dc.subjectInformation Systems
dc.subjectGeography, Planning and Development
dc.titleIdentification of low accuracy regions in land cover maps using uncertainty measures and classification confidenceen
dc.typeconference object
degois.publication.firstPage275
degois.publication.issue4
degois.publication.lastPage281
degois.publication.titleSPRS TC IV Mid-term Symposium “3D Spatial Information Science – The Engine of Change”
degois.publication.titleISPRS TC IV Mid-Term Symposium on 3D Spatial Information Science - The Engine of Change
degois.publication.volume42
dspace.entity.typePublication
rcaap.rightsopenAccess

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