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dc.contributor.authorHu, Dingsheng
dc.contributor.authorDoulgeris, Anthony Paul
dc.contributor.authorQiu, Xiaolan
dc.date.accessioned2018-11-14T12:55:34Z
dc.date.available2018-11-14T12:55:34Z
dc.date.issued2015-11-12
dc.description.abstractThis paper introduces a novel unsupervised estimator of equivalent number of looks (ENL) that can be applied to an arbitrary image. It avoids the assumption that homogeneous speckle will dominate the investigated image that is followed by current unsupervised ENL estimators but not always valid, especially for the complex SAR scenes with high mixture and texture. Incorporating the statistical properties of ENL data into an automatic segmentation method, we isolate the sub-class affected least by mixture and texture and suggest taking the mean value of this class as the final ENL estimate. The proposed estimator is evaluated in the experiments performed on simulated and real data from two very different sensors. It always gives better results than the other two existing methods and possesses greater adaptability.en_US
dc.descriptionThis is the accepted manuscript version of the following article: Hu, D., Doulgeris, A.P. & Qiu, X. (2015). An unsupervised method for equivalent number of looks estimation in complex SAR scenes. 2015 <i>IEEE International Geoscience and Remote Sensing Symposium (IGARSS)</i>. https://doi.org/10.1109/IGARSS.2015.7326309. Published version available at <a href=https://doi.org/10.1109/IGARSS.2015.7326309> https://doi.org/10.1109/IGARSS.2015.7326309</a>.en_US
dc.identifier.citationHu, D., Doulgeris, A.P. & Qiu, X. (2015). An unsupervised method for equivalent number of looks estimation in complex SAR scenes. <i>2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)</i>. https://doi.org/10.1109/IGARSS.2015.7326309en_US
dc.identifier.cristinIDFRIDAID 1320649
dc.identifier.urihttps://hdl.handle.net/10037/14167
dc.language.isoengen_US
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.rights.accessRightsopenAccessen_US
dc.subjectVDP::Mathematics and natural science: 400::Physics: 430en_US
dc.subjectVDP::Matematikk og Naturvitenskap: 400::Fysikk: 430en_US
dc.subjectMaximum likelihood estimationen_US
dc.subjectSynthetic aperture radaren_US
dc.subjectHistogramsen_US
dc.subjectData modelsen_US
dc.subjectImage segmentationen_US
dc.subjectRobustnessen_US
dc.titleAn unsupervised method for equivalent number of looks estimation in complex SAR scenesen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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