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Article: SAR image despeckling with a multilayer perceptron neural network

TitleSAR image despeckling with a multilayer perceptron neural network
Authors
Keywordssynthetic aperture radar
neural network
Multilayer perceptron
despeckling
Issue Date2018
Citation
International Journal of Digital Earth, 2018, p. 1-21 How to Cite?
Abstract© 2018 Informa UK Limited, trading as Taylor & Francis Group Speckle noise in synthetic-aperture radar (SAR) images severely hinders remote sensing applications; therefore, the appropriate removal of speckle noise is crucial. This paper elaborates on the multilayer perceptron (MLP) neural-network model for SAR image despeckling by using a time series of SAR images. Unlike other filtering methods that use only a single radar intensity image to derive their parameters and filter that single image, this method can be trained using archived images over an area of interest to self-learn the intensity characteristics of image patches and then adaptively determine the weights and thresholds by using a neural network for image despeckling. Several hidden layers are designed for feedforward network training, and back-propagation stochastic gradient descent is adopted to reduce the error between the target output and neural-network output. The parameters in the network are automatically updated in the training process. The greatest advantage of MLP is that once the despeckling parameters are determined, they can be used to process not only new images in the same area but also images in completely different locations. Tests with images from TerraSAR-X in selected areas indicated that MLP shows satisfactory performance with respect to noise reduction and edge preservation. The overall image quality obtained using MLP was markedly higher than that obtained using numerous other filters. In comparison with other recently developed filters, this method yields a slightly higher image quality, and it demonstrates the powerful capabilities of computer learning using SAR images, which indicate the promising prospect of applying MLP to SAR image despeckling.
Persistent Identifierhttp://hdl.handle.net/10722/266823
ISSN
2017 Impact Factor: 2.746
2015 SCImago Journal Rankings: 1.106
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorTang, Xiao-
dc.contributor.authorZhang, Lei-
dc.contributor.authorDing, Xiaoli-
dc.date.accessioned2019-01-31T07:19:42Z-
dc.date.available2019-01-31T07:19:42Z-
dc.date.issued2018-
dc.identifier.citationInternational Journal of Digital Earth, 2018, p. 1-21-
dc.identifier.issn1753-8947-
dc.identifier.urihttp://hdl.handle.net/10722/266823-
dc.description.abstract© 2018 Informa UK Limited, trading as Taylor & Francis Group Speckle noise in synthetic-aperture radar (SAR) images severely hinders remote sensing applications; therefore, the appropriate removal of speckle noise is crucial. This paper elaborates on the multilayer perceptron (MLP) neural-network model for SAR image despeckling by using a time series of SAR images. Unlike other filtering methods that use only a single radar intensity image to derive their parameters and filter that single image, this method can be trained using archived images over an area of interest to self-learn the intensity characteristics of image patches and then adaptively determine the weights and thresholds by using a neural network for image despeckling. Several hidden layers are designed for feedforward network training, and back-propagation stochastic gradient descent is adopted to reduce the error between the target output and neural-network output. The parameters in the network are automatically updated in the training process. The greatest advantage of MLP is that once the despeckling parameters are determined, they can be used to process not only new images in the same area but also images in completely different locations. Tests with images from TerraSAR-X in selected areas indicated that MLP shows satisfactory performance with respect to noise reduction and edge preservation. The overall image quality obtained using MLP was markedly higher than that obtained using numerous other filters. In comparison with other recently developed filters, this method yields a slightly higher image quality, and it demonstrates the powerful capabilities of computer learning using SAR images, which indicate the promising prospect of applying MLP to SAR image despeckling.-
dc.languageeng-
dc.relation.ispartofInternational Journal of Digital Earth-
dc.subjectsynthetic aperture radar-
dc.subjectneural network-
dc.subjectMultilayer perceptron-
dc.subjectdespeckling-
dc.titleSAR image despeckling with a multilayer perceptron neural network-
dc.typeArticle-
dc.description.natureLink_to_subscribed_fulltext-
dc.identifier.doi10.1080/17538947.2018.1447032-
dc.identifier.scopuseid_2-s2.0-85043342578-
dc.identifier.spage1-
dc.identifier.epage21-
dc.identifier.eissn1753-8955-
dc.identifier.isiWOS:000457412600006-

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