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Conference Paper: A comparison of SAR image speckle filters

TitleA comparison of SAR image speckle filters
Authors
KeywordsSar Image
Speckle Filters
Issue Date2009
PublisherS P I E - International Society for Optical Engineering. The Journal's web site is located at http://spie.org/x1848.xml
Citation
Remote Sensing and GIS Data Processing and Other Applications: 6th International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR 2009), Yichang, China, 30 October-1 November 2009. In Proceedings of SPIE - International Society for Optical Engineering, 2009, v. 7498, p. article no. 749804 How to Cite?
AbstractHigh quality images of Earth produced by synthetic aperture radar (SAR) systems have become increasingly available, however, SAR images are difficult to interpret. Speckle reduction remains one of the major issues in SAR imaging process, although speckle has been extensively studied for decades. Many reconstruction filters have been proposed and they can be classified into two categories: multilook and/or minimum mean-square error (MMSE) despeckling using the speckle model; and maximum a posteriori (MAP) or maximum likihood (ML) despeckling using the product model. The most well known Lee, Kuan, and Frost filters belong to first category. These filters are based on conventional techniques that were originally derived for stationary signals, such as MMSE. In the second category, filters are based on the product model, such as the MAP Gaussian filter and the Gamma filter, and require knowledge of the a priori probability density function. These filters force speckle to have nonstationary Gaussian or gamma distributed intensity mean. The speckle filtering is mainly Bayesian model fitting that optimizes the MAP criteria. Scene reconstruction is performed using an inversion of the ascending chain. An objective measure is required to compare the technical merits of these filters, and Shi et al. presented a comparison 15 years ago. In this paper, a brief introduction of speckle, product, and filter models is summarized. A review of some most widely used SAR image speckle filters is given. And stationary speckle filters, like Lee, Kuan, and Frost filters, and nonstationary speckle filters like Gamma MAP filter are studied. Despeckling results on stationary and nonstationary SAR image of these speckle filters are presented. © 2009 Copyright SPIE - The International Society for Optical Engineering.
Persistent Identifierhttp://hdl.handle.net/10722/158609
ISBN
ISSN
References

 

DC FieldValueLanguage
dc.contributor.authorLang, Sen_US
dc.contributor.authorLin, CYen_US
dc.contributor.authorLiu, Jen_US
dc.contributor.authorWong, Nen_US
dc.contributor.authorSo, HKHen_US
dc.date.accessioned2012-08-08T09:00:28Z-
dc.date.available2012-08-08T09:00:28Z-
dc.date.issued2009en_US
dc.identifier.citationRemote Sensing and GIS Data Processing and Other Applications: 6th International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR 2009), Yichang, China, 30 October-1 November 2009. In Proceedings of SPIE - International Society for Optical Engineering, 2009, v. 7498, p. article no. 749804en_US
dc.identifier.isbn9780819478092-
dc.identifier.issn0277-786Xen_US
dc.identifier.urihttp://hdl.handle.net/10722/158609-
dc.description.abstractHigh quality images of Earth produced by synthetic aperture radar (SAR) systems have become increasingly available, however, SAR images are difficult to interpret. Speckle reduction remains one of the major issues in SAR imaging process, although speckle has been extensively studied for decades. Many reconstruction filters have been proposed and they can be classified into two categories: multilook and/or minimum mean-square error (MMSE) despeckling using the speckle model; and maximum a posteriori (MAP) or maximum likihood (ML) despeckling using the product model. The most well known Lee, Kuan, and Frost filters belong to first category. These filters are based on conventional techniques that were originally derived for stationary signals, such as MMSE. In the second category, filters are based on the product model, such as the MAP Gaussian filter and the Gamma filter, and require knowledge of the a priori probability density function. These filters force speckle to have nonstationary Gaussian or gamma distributed intensity mean. The speckle filtering is mainly Bayesian model fitting that optimizes the MAP criteria. Scene reconstruction is performed using an inversion of the ascending chain. An objective measure is required to compare the technical merits of these filters, and Shi et al. presented a comparison 15 years ago. In this paper, a brief introduction of speckle, product, and filter models is summarized. A review of some most widely used SAR image speckle filters is given. And stationary speckle filters, like Lee, Kuan, and Frost filters, and nonstationary speckle filters like Gamma MAP filter are studied. Despeckling results on stationary and nonstationary SAR image of these speckle filters are presented. © 2009 Copyright SPIE - The International Society for Optical Engineering.en_US
dc.languageengen_US
dc.publisherS P I E - International Society for Optical Engineering. The Journal's web site is located at http://spie.org/x1848.xmlen_US
dc.relation.ispartofProceedings of SPIE - The International Society for Optical Engineeringen_US
dc.rightsProceedings of SPIE - International Society for Optical Engineering. Copyright © S P I E - International Society for Optical Engineering.-
dc.rightsCopyright notice format: Copyright 2009 Society of Photo-Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subjectSar Imageen_US
dc.subjectSpeckle Filtersen_US
dc.titleA comparison of SAR image speckle filtersen_US
dc.typeConference_Paperen_US
dc.identifier.emailWong, N:nwong@eee.hku.hken_US
dc.identifier.emailSo, HKH:hso@eee.hku.hken_US
dc.identifier.authorityWong, N=rp00190en_US
dc.identifier.authoritySo, HKH=rp00169en_US
dc.description.naturepublished_or_final_versionen_US
dc.identifier.doi10.1117/12.830946en_US
dc.identifier.scopuseid_2-s2.0-71549144084en_US
dc.identifier.hkuros236649-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-71549144084&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume7498en_US
dc.publisher.placeUnited Statesen_US
dc.identifier.scopusauthoridLang, S=23492736000en_US
dc.identifier.scopusauthoridLin, CY=35177986900en_US
dc.identifier.scopusauthoridLiu, J=35208524300en_US
dc.identifier.scopusauthoridWong, N=35235551600en_US
dc.identifier.scopusauthoridSo, HKH=10738896800en_US

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