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姓名 溫仁宏(Jen-Hung Wen)  查詢紙本館藏   畢業系所 太空科學研究所
論文名稱 改進式Sigma filter應用於雷達影像斑駁抑制
(Improving sigma filter for speckle reduction of SAR images)
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摘要(中) 合成孔徑雷達影像(SAR)影像上呈現亮暗點混雜般的粒狀雜訊,稱為斑駁現象( Speckle),其成因來自於接收地表許多散射體的回波訊號,並產生不同相位的干涉所導致。斑駁現象可導致影像判識上的困難,且降低雷達影像分割及分類的效果,目前已研究發展出許多的演算法用以減輕或抑制雷達影像斑駁現象,其中基於Rayleigh機率分佈型式所發展出的Refined Lee filter,以有效保留影像的邊緣線性特徵及影像解析力的特性,受到廣泛地的運用。其他常使用的演算法則是sigma filter,它是基於高斯機率分佈內的2倍標準差區間所開發獲得的,而此方法具有在機率分佈不對稱及亮點模糊等缺點。本論文旨在改進sigma filter的不足之處,並測試改進的效果,並與Refined Lee filter進行比對。
本研究是以sigma filter的基本概念推導而來,效果不足之處如下:(1)不同於高斯分佈具左右對稱於其分佈平均值的特性,在雷達影像斑駁機率分佈是不對稱的而造成標準差區間的不對稱估算;(2)抑制結果影像中出現許多單獨的暗點(非零值),來自於小的標準差區間將排除較亮的像元;(3)較大的標準差區間將包括所有的像元而造成亮點模糊。故本研究擬對上述缺點進行修正,可分為兩階段進行:第一階段,為重新估算斑駁機率分佈區間,並保持其分佈平均值;同時選擇最佳的初始分佈平均值;第一階段的斑駁抑制效果雖令人滿意,卻有解析度下降、明暗線段消失及亮點區域模糊化等問題。因此第二階段針對上述問題,提出“改進式sigma filter”演算法內加入MMSE (Minimum Mean Square Error) 方法,及重新估算受不同標準差區間而改變的影像斑駁理想標準差,以克服解析度下降及明暗線段消失現象;在保留影像區塊亮點部份,設定門檻值以濾除單一亮點的斑駁現象。最後利用日本ALOS /PALSAR 及 USA JPL/AIRSAR影像驗證“改進式sigma filter”演算法於斑駁抑制及邊緣保留的效果。
摘要(英) Speckle, appearing in Synthetic Aperture Radar (SAR) image as granular noise of mixed bright and dark pixels, is due to coherent interference of backscattered wavelets from scatters with their phases randomly distributed in a resolution cell. Speckle in SAR image complicates the image interpretation problem by reducing the effectiveness of image segmentation and classification. To alleviate the speckle effect, many algorithms have been devised to suppress speckle. Among them, the Refined Lee filter which was developed based on Rayleigh probability model has received widespread acceptance, because of its excellent characteristics of smoothing speckle noise while preserving edges, line features and image resolution. Another frequent applied algorithm is the sigma filter. The sigma filter was developed based on the two sigma range of Gaussian distribution. However, its deficiencies of introducing bias and blurring bright targets make it less desirable ,especially for single–look SAR data. In this thesis, an improved sigma filter was developed, and our test results show that its effectiveness is comparable to the Refined Lee filter.
This research is derived from the basic concept of sigma filter. The sigma filter is known to have the following deficiencies:(1) the estimated amplitudes or intensities are biased , because , unlike Gaussian distribution, the speckle distribution is not symmetrical about its mean, (2) many isolated dark pixels remain not filtered, because of their small sigma range that excludes other brighter pixels, (3) bright point targets are smeared due to the large sigma range that includes all pixels. To compensate for these deficiencies, we devised an improved sigma filter. We divided the development process into two stages. In the first stage, sigma intervals were recomputed base on SAR speckle distributions to maintain their mean values in the filtered image, and different ways to estimate the initial mean value were implemented. The first two deficiencies were successfully eliminated, but bright edges and targets were blurred due to losing image resolution, So in the second stage, we incorporated the MMSE (Minimum Mean Square Error) Method into the sigma filter to overcome the losing image resolution problem. In addition, to preserve the bright targets, a threshold was established to retain bright areas containing more than three pixels.
The improved Sigma filter successfully compensated for the deficiencies of the original sigma filter. For illustration, the improved sigma filter was tested and evaluated using SAR data from ALOS/PALSAR and JPL/AIRSAR. Reasonably good results of speckle reduction and edge preserving were obtained.
關鍵字(中) ★ 最小平均方根誤差
★ 標準差區間
★ 隨機散射場
關鍵字(英) ★ Sigma Interval
★ Random Filed
★ Minimum Mean Square Error
論文目次 中文摘要..................................................Ⅰ
英文摘要..................................................Ⅱ
目錄......................................................Ⅴ
表目......................................................Ⅶ
圖目錄....................................................Ⅷ
第一章 簡介.............................................1
1.1 斑駁雜訊現象及濾除抑制...........................2
1.2 文獻回顧與研究方法概述...........................4
1.3 論文整體架構.....................................6
第二章 合成孔徑雷達影像概述及斑駁雜訊數學模型...........7
2.1 SAR影像成像原理................................. 7
2.2 SAR影像基本特性.................................12
2.3 SAR影像斑駁數學模型.............................16
2.3.1 隨機散射場概述(RANDOM FIELD)................17
2.3.2 估算斑駁雜訊理想標準差..................... 19
第三章 濾波演算法架構................................. 22
3.1 MMSE filter 數學模型............................22
3.2 REFINED Lee filter數學模型..................... 24
3.3 SIGMA filter 數學模型...........................27
3.4 斑駁抑制結果及討論............................. 31
第四章 改進式sigma filter濾波器的概念及產生........... 33
4.1 第一階段修正方法............................... 33
4.1.1 sigma區間重新估算方法.......................33
4.1.2 採用不同機率分佈函數平均值M(z) ............ 36
4.1.3 第一階段結果展示及討論..................... 38
4.2 第二階段修正方法............................... 40
4.2.1   MMSE filter模型植入.........................40
4.2.2   重新估算斑駁理想標準差 .....................41
4.2.3 亮點區域保留方法........................... 43
第五章 測試資料及成果展示............................. 45
5.1 測試影像資料簡介............................... 45
5.2 斑駁抑制效果評估方法........................... 47
5.2.1 濾除影像及量化數據展示..................... 47
5.2.2 模擬影像測試............................... 59
5.2.3 亮點區域保留法測試..........................62
5.3 結果評鑑....................................... 66
第六章 結論........................................... 67
參考文獻................................................ 69
參考文獻 [1] J . W . Goodman , “ Some fundamental properties of speckle , ” J .Opt . Sco . Am , 66 (11),p1145-1150,1976.
[2]J.S.Lee,et al.“Speckle Filtering of SAR images:A Reviews,"Remote Sensig Reviews , Vol.8,PP,313-340,1994.
[3]J.S.Lee,“Digital image enhancement and noise filtering by use of local statistics,” IEEE Trans . PAMI ,Vol.2 No.2,1980.
[4]D.T.Kwan,et al.“Adaptive Noise filtering for Images with Signal dependent noise,” IEEE Trans.PAMI, Vol.7,No.2,1985.
[5]A.Lopes,et al.“Adaptive speckle filters and scene homogeneity,”IEEE TGARS,Vol.28,No.6,1990.
[6]J.S.Lee,“ Refined filtering of image noise using local statistics,“ CVGIP , vol .15,p380-389,1981.
[7]J.S.Lee,“Digtal image noise smoothing and the sigma filter,” CVGIP, Vol. 24, p255-269,1983
[8]J.S.Lee,“A Simple speckle smoothing algorithm for SAR images,”IEEE Trans . SMC ,Vol.13 No.1,1983.
[9]J.S.Lee,K.Hoppel and S.A.Mango,“Unsupervised Estimation of Speckle Noise in Radar images,” Int.J.of Imag.Sys. and Tech., vol.4,p298-305,1992.
[10]Ulaby F.,Moore,R. and A. Fung, Microwave Remote Sensing, Vol. 3, Artec House,Norwood,MA,1986.
指導教授 李仲森、陳錕山
(Jong-Sen Lee、Kun-Shan Chen)
審核日期 2007-7-19
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