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http://dx.doi.org/10.7780/kjrs.2009.25.5.455

Despeckling and Classification of High Resolution SAR Imagery  

Lee, Sang-Hoon (Department of Industrial Engineering, KyungWon University)
Publication Information
Korean Journal of Remote Sensing / v.25, no.5, 2009 , pp. 455-464 More about this Journal
Abstract
Lee(2009) proposed the boundary-adaptive despeckling method using a Bayesian model which is based on the lognormal distribution for image intensity and a Markov random field(MRF) for image texture. This method employs the Point-Jacobian iteration to obtain a maximum a posteriori(MAP) estimate of despeckled imagery. The boundary-adaptive algorithm is designed to use less information from more distant neighbors as the pixel is closer to boundary. It can reduce the possibility to involve the pixel values of adjacent region with different characteristics. The boundary-adaptive scheme was comprehensively evaluated using simulation data and the effectiveness of boundary adaption was proved in Lee(2009). This study, as an extension of Lee(2009), has suggested a modified iteration algorithm of MAP estimation to enhance computational efficiency and to combine classification. The experiment of simulation data shows that the boundary-adaption results in yielding clear boundary as well as reducing error in classification. The boundary-adaptive scheme has also been applied to high resolution Terra-SAR data acquired from the west coast of Youngjong-do, and the results imply that it can improve analytical accuracy in SAR application.
Keywords
despeckling; Point-Jacobian iteration; boundary-adaptive; Bayesian Model; classification;
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Times Cited By KSCI : 3  (Citation Analysis)
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