Korean Journal of Remote Sensing (대한원격탐사학회지)

Korean Society of Remote Sensing (대한원격탐사학회)
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Despeckling and Classification of High Resolution SAR Imagery

고해상도 SAR 영상 Speckle 제거 및 분류

  • Lee, Sang-Hoon (Department of Industrial Engineering, KyungWon University)
  • Published : 2009.10.31
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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.

Lee(2009)에서 영상 강도를 위해서 lognormal 확률 모형과 영상 texture를 위해서 Markov random field(MRF)에 기반하는 Bayesian 모형을 사용하는 boundary-adaptive despeckling 방법을 제안하였다. 이 방법은 speckle 제거 영상의 최대 사후(maximum a posteriori: MAP) 추정치를 구하기 위해서 Point-Jacobian iteration을 이용한다 인접하고 있는 다른 특성의 지역에 위치한 화소의 값을 사용하는 가능성을 줄이기 위해 Boundary-adaptive algorithm은 경계에 가까울 수록 멀리 떨어진 이웃 화소로부터 정보를 덜 수집하도록 고안된다. 이러한 boundary-adaptive 방법은 전반적으로 simulation 자료를 사용하여 Lee(2009)에서 평가되었고 그리고 제안된 방법의 효험을 증명하였다. 본 연구는 Lee(2009)의 확장 연구로 MAP 추정치를 구하기 반복 algorithm의 계산 효율성을 증가 시키고 noise 제거와 함께 분류를 수행하는 수정 algorithm을 제안한다. Simulation 자료를 사용한 실험을 통해서 boundary-adaption이 분류 오류를 줄여줄 뿐 아니라 더욱 명확한 경계선을 보여준다는 것을 알 수 있다. 또한 영종도 서해안에서 관측된 고해상도 Terra-SAR data에 적용한 결과는 boundary-adaption은 SAR 활용에서 분석의 정확성을 개선 시킬 수 있다는 것을 암시한다.

Keywords

References

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