結合多尺度邊緣檢測的SAR結構鄰域濾波方法

酈蘇丹; 李廣俠

結合多尺度邊緣檢測的SAR結構鄰域濾波方法

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出版歷程
- 收稿日期: 2004-12-09
- 修回日期: 2005-04-29
- 刊出日期: 2006-08-19

A SAR Image Filter Method Based on Structured Neibourhood Model with Multi-scale Edges Detection

摘要

摘要: 噪聲抑制是合成孔徑雷達(SAR)圖像處理的一個重要環(huán)節(jié)，通常的方法在抑制噪聲的同時也使得圖像的邊緣模糊。該文提出一種結合宏觀邊緣信息的SAR圖像結構鄰域濾波方法。首先利用多尺度邊緣檢測算法獲得SAR圖像主要的邊緣信息，在此基礎上對結構鄰域濾波法進行改進。結構鄰域濾波法用一系列反映圖像局部方向信息的鄰域模板描述圖像的細節(jié)特性，濾波過程中引入圖像的宏觀邊緣信息，對鄰域模板的選擇范圍進行約束。最后運用模擬退火算法選取合適鄰域模板對目標點的強度進行MAP估計。實驗表明該方法能夠較好地保持圖像的邊緣特征，同時有效抑制了斑點噪聲。
- SAR;濾波;結構鄰域模型;多尺度;邊緣檢測
Abstract: Speckle reducing is an important step in Synthetic Aperture Radar (SAR) image processing, ordinary methods blur image edges while reducing speckle. A SAR image filter method based on structured neibourhood model with macroscopical edge information is proposed in this paper. First multi-scale edge detector is applied to achieve principal image edges, then structured neibourhood filter is modified and used. Structured neibourhood filter adopts a series of templates reflecting regional direction information to describe image detailed property, the macroscopical edge information is introduced to constrain the selection of templates while processing. Finally simulated annealing algorithm is used to get appropriate template, the MAP estimate of the object pixel intensity can be achieved. Experiment indicates this method reduces effectively speckle while preserving image edges.

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Shi Zhenghao, Fung K B. A comparison of digital speckle filters [C]. IGARSS94: 2129-2134.[2]Lopes A, Nezry E, Touzi R. Structure detection and statistical adaptive speckle filtering in SAR images [J].Int. J. of Remote Sensing.1993, 14(9):1735-1758[3]Ulaby F T, Kouyate F. Texture information in SAR images [J]. IEEE Trans. on GRS, 1986, 24(2): 235-245.[4]Lee J S. Speckle analysis and smoothing of synthetic aperture radar images[J].Computer Graphic and Image Processing.1981, 17(1):24-32[5]Kuan D T, Sawchuk A A. Adaptive noise smoothing filter for signal-dependent noise[J]. IEEE Trans. on PAMI, 1985, 7(2): 165-177.[6]Frost V S, Stiles J A. A model for radar images and its application to adaptive digital filtering of multiplicative noise [J]. IEEE Trans. on PAMI, 1982, 4(2): 157-166.[7]Lopes A, Tonzi R, Nezry E. Adaptive speckle filter and sense heterogeneity[J]. IEEE Trans. on GRS, 1990, 28(6): 992-1000.[8]Baraldi A, Parmiggiani F. A refined gamma MAP SAR speckle filter with improved geometrical adaptivity[J]. IEEE Trans. on GRS, 1995, 33(5): 1245-1257.[9]Marc S，Gianfranco D G. Analysis of speckle noise contribution on wavelet decomposition of SAR images[J]. IEEE Trans. on GRS, 1998, 36(6): 1953-1962.[10]Dong Y, Milne A K, Forster B C. Toward edge sharpening: a SAR speckle filtering algorithm [J]. IEEE Trans. on GRS. 2001, 39(4): 851-863.[11]Han C M, Guo H D, Wang C L, et al.. A novel method to reduce speckle in SAR images [J].Int. J. of Remote Sensing.2002, 23(23):5095-5101[12]韓春明，郭華東. 一種改進的SAR圖像斑點噪聲濾波方法[J].遙感學報，2004，8(2): 121-127.[13]Mallat S, Hwang W L. Singularity detection and processing with wavelets[J].IEEE. Trans. on Information Theory.1992, 38(3):617-643[14]酈蘇丹，李廣俠. SAR圖像的多尺度邊緣檢測方法[J]. 系統工程與電子技術，2004，26(3): 307-310.[15]Oliver C J, Quegan S. Understanding synthetic aperture radar images[M], Washington: Artech House Inc, 1998, Chapter 5.[16]邢文訓，謝金星. 現代優(yōu)化計算方法[M]. 北京：清華大學出版社，1999，第3章.

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