Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/222510
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dc.contributor.authorFeng Xuen_US
dc.contributor.authorQian Songen_US
dc.contributor.authorYa-Qiu Jinen_US
dc.date.accessioned2013en_US
dc.date.accessioned2020-04-06T08:20:24Z-
dc.date.available2020-04-06T08:20:24Z-
dc.date.issued2017en_US
dc.identifier.other10.1109/TGRS.2017.2700951en_US
dc.identifier.urihttp://localhost/handle/Hannan/222510-
dc.description.abstractThis paper reformulates the problem of polarimetric incoherent target decomposition as a general image factorization which aims to simultaneously estimate a dictionary of meaningful atom scatterers and their corresponding spatial distribution maps. Both model-based and eigenanalysis-based decompositions can be seen as special cases of image factorization under specific constraints. The inverse problem of image factorization can be converted to an equivalent nonnegative matrix factorization (NMF) problem via redundant coding. It enables a wide range of NMF algorithms with various regularizations to be directly applicable to polarimetric image analysis. The advantage of the proposed image factorization is demonstrated on both synthesized and real data. It also shows that extended applications such as speckle reduction and classification can benefit from the proposed image factorization.en_US
dc.format.extent5026,en_US
dc.format.extent5041en_US
dc.publisherIEEEen_US
dc.relation.haspart7941997.pdfen_US
dc.titlePolarimetric SAR Image Factorizationen_US
dc.typeArticleen_US
dc.journal.volume55en_US
dc.journal.issue9en_US
Appears in Collections:2017

Files in This Item:
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7941997.pdf4.63 MBAdobe PDF
Full metadata record
DC FieldValueLanguage
dc.contributor.authorFeng Xuen_US
dc.contributor.authorQian Songen_US
dc.contributor.authorYa-Qiu Jinen_US
dc.date.accessioned2013en_US
dc.date.accessioned2020-04-06T08:20:24Z-
dc.date.available2020-04-06T08:20:24Z-
dc.date.issued2017en_US
dc.identifier.other10.1109/TGRS.2017.2700951en_US
dc.identifier.urihttp://localhost/handle/Hannan/222510-
dc.description.abstractThis paper reformulates the problem of polarimetric incoherent target decomposition as a general image factorization which aims to simultaneously estimate a dictionary of meaningful atom scatterers and their corresponding spatial distribution maps. Both model-based and eigenanalysis-based decompositions can be seen as special cases of image factorization under specific constraints. The inverse problem of image factorization can be converted to an equivalent nonnegative matrix factorization (NMF) problem via redundant coding. It enables a wide range of NMF algorithms with various regularizations to be directly applicable to polarimetric image analysis. The advantage of the proposed image factorization is demonstrated on both synthesized and real data. It also shows that extended applications such as speckle reduction and classification can benefit from the proposed image factorization.en_US
dc.format.extent5026,en_US
dc.format.extent5041en_US
dc.publisherIEEEen_US
dc.relation.haspart7941997.pdfen_US
dc.titlePolarimetric SAR Image Factorizationen_US
dc.typeArticleen_US
dc.journal.volume55en_US
dc.journal.issue9en_US
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7941997.pdf4.63 MBAdobe PDF
Full metadata record
DC FieldValueLanguage
dc.contributor.authorFeng Xuen_US
dc.contributor.authorQian Songen_US
dc.contributor.authorYa-Qiu Jinen_US
dc.date.accessioned2013en_US
dc.date.accessioned2020-04-06T08:20:24Z-
dc.date.available2020-04-06T08:20:24Z-
dc.date.issued2017en_US
dc.identifier.other10.1109/TGRS.2017.2700951en_US
dc.identifier.urihttp://localhost/handle/Hannan/222510-
dc.description.abstractThis paper reformulates the problem of polarimetric incoherent target decomposition as a general image factorization which aims to simultaneously estimate a dictionary of meaningful atom scatterers and their corresponding spatial distribution maps. Both model-based and eigenanalysis-based decompositions can be seen as special cases of image factorization under specific constraints. The inverse problem of image factorization can be converted to an equivalent nonnegative matrix factorization (NMF) problem via redundant coding. It enables a wide range of NMF algorithms with various regularizations to be directly applicable to polarimetric image analysis. The advantage of the proposed image factorization is demonstrated on both synthesized and real data. It also shows that extended applications such as speckle reduction and classification can benefit from the proposed image factorization.en_US
dc.format.extent5026,en_US
dc.format.extent5041en_US
dc.publisherIEEEen_US
dc.relation.haspart7941997.pdfen_US
dc.titlePolarimetric SAR Image Factorizationen_US
dc.typeArticleen_US
dc.journal.volume55en_US
dc.journal.issue9en_US
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7941997.pdf4.63 MBAdobe PDF