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dc.contributor.authorPajares Martinsanz, Gonzalo
dc.contributor.authorLópez Martínez, Carlos
dc.contributor.authorSánchez Lladó, Francisco Javier
dc.contributor.authorMolina, Iñigo
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions
dc.date.accessioned2016-05-18T15:12:20Z
dc.date.issued2012-11-19
dc.identifier.citationPajares, G., Lopez, C., Sánchez, F., Molina, I. Improving wishart classification of polarimetric SAR data using the hopfield neural network optimization approach. "Remote sensing", 19 Novembre 2012, vol. 4, núm. 11, p. 3571-3595.
dc.identifier.issn2072-4292
dc.identifier.urihttp://hdl.handle.net/2117/87165
dc.description.abstractThis paper proposes the optimization relaxation approach based on the analogue Hopfield Neural Network (HNN) for cluster refinement of pre-classified Polarimetric Synthetic Aperture Radar (PolSAR) image data. We consider the initial classification provided by the maximum-likelihood classifier based on the complex Wishart distribution, which is then supplied to the HNN optimization approach. The goal is to improve the classification results obtained by the Wishart approach. The classification improvement is verified by computing a cluster separability coefficient and a measure of homogeneity within the clusters. During the HNN optimization process, for each iteration and for each pixel, two consistency coefficients are computed, taking into account two types of relations between the pixel under consideration and its corresponding neighbors. Based on these coefficients and on the information coming from the pixel itself, the pixel under study is re-classified. Different experiments are carried out to verify that the proposed approach outperforms other strategies, achieving the best results in terms of separability and a trade-off with the homogeneity preserving relevant structures in the image. The performance is also measured in terms of computational central processing unit (CPU) times.
dc.format.extent25 p.
dc.language.isoeng
dc.subjectÀrees temàtiques de la UPC::Enginyeria de la telecomunicació::Radiocomunicació i exploració electromagnètica::Radar
dc.subject.lcshSyntethic aperture radar
dc.subject.lcshEllipsometry
dc.subject.lcshNeural networks (Computer science)
dc.subject.otherHopfield neural networks
dc.subject.otherImage classification
dc.subject.otherOptimization
dc.subject.otherPolarimetric synthetic aperture radar (PolSAR)
dc.subject.otherWishart classifier
dc.titleImproving wishart classification of polarimetric SAR data using the hopfield neural network optimization approach
dc.typeArticle
dc.subject.lemacRadar d'obertura sintètica
dc.subject.lemacEl·lipsometria
dc.subject.lemacXarxes neuronals (Informàtica)
dc.contributor.groupUniversitat Politècnica de Catalunya. RSLAB - Grup de Recerca en Teledetecció
dc.identifier.doi10.3390/rs4113571
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttp://www.mdpi.com/2072-4292/4/11/3571
dc.rights.accessRestricted access - publisher's policy
local.identifier.drac11074204
dc.description.versionPostprint (published version)
dc.date.lift10000-01-01
local.citation.authorPajares, G.; Lopez, C.; Sánchez, F.; Molina, I.
local.citation.publicationNameRemote sensing
local.citation.volume4
local.citation.number11
local.citation.startingPage3571
local.citation.endingPage3595


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