基于幾何活動(dòng)輪廓模型的SAR圖像海岸線檢測(cè)
發(fā)布時(shí)間：2018-09-12 06:20
【摘要】：合成孔徑雷達(dá)(Synthetic Aperture Radar,SAR)圖像海岸線檢測(cè)在海岸線管理、地圖自動(dòng)導(dǎo)航、船艦?zāi)繕?biāo)識(shí)別等方面發(fā)揮著重要的作用。幾何活動(dòng)輪廓(Geometric Active Contour,GAC)模型是在活動(dòng)輪廓(Active Contour Model,ACM,又稱為Snake模型)模型的基礎(chǔ)上發(fā)展起來的,Snake模型是提取圖像邊界領(lǐng)域的重大突破性的發(fā)展,而且有非常實(shí)用的研究?jī)r(jià)值。近幾年,隨著Snake模型的廣泛深入研究,GAC模型的思想受到了世界上廣泛的關(guān)注,涉及的領(lǐng)域也越來越廣。GAC模型在提取SAR圖像邊界的領(lǐng)域上也顯示出強(qiáng)大的實(shí)用性。但是由于SAR圖像具有邊界模糊、對(duì)比度小、灰度等級(jí)多并且易受噪聲干擾等問題,GAC模型的方法處理SAR圖像仍然會(huì)遇到一些弱邊界問題、迭代次數(shù)和迭代時(shí)間易受圖像初始輪廓影響以及圖像預(yù)處理對(duì)提取SAR圖像的海岸線造成影響的問題。針對(duì)此問題,本文以SAR圖像海岸線檢測(cè)為應(yīng)用背景,對(duì)其中涉及的弱邊界問題、圖像初始輪廓影響海岸線檢測(cè)的迭代次數(shù)和迭代時(shí)間的問題及圖像預(yù)處理對(duì)海岸線檢測(cè)的影響進(jìn)行了系統(tǒng)研究。經(jīng)過研究SAR圖像海岸線檢測(cè)弱邊界的特點(diǎn),提出利用結(jié)合區(qū)域信息的改進(jìn)符號(hào)壓力函數(shù)為GAC模型的邊界停止條件并對(duì)海岸線進(jìn)行精確提取,這樣能很好的彌補(bǔ)SAR圖像中海岸線弱邊界的不足,使得提取出的海岸線更加準(zhǔn)確。在提取SAR圖像海岸線存在弱邊界問題進(jìn)行研究的基礎(chǔ)上,本文也對(duì)SAR圖像初始輪廓的選取、圖像預(yù)處理對(duì)提取出的海岸線的影響進(jìn)行了研究。本文將研究SAR圖像不同大小的初始輪廓對(duì)海岸線檢測(cè)的迭代次數(shù)及迭代時(shí)間的影響,這是基于GAC模型不敏感于圖像的初始輪廓。圖像的初始輪廓選取越大,GAC模型的迭代次數(shù)越少,迭代次數(shù)越少則迭代時(shí)間越短。在圖像的預(yù)處理中,因?yàn)镾AR圖像中的斑點(diǎn)噪聲是乘性的,所以一般的圖像增強(qiáng)方法、去除噪聲方法已不適用于SAR圖像,本文用灰度變換的方法對(duì)SAR圖像進(jìn)行增強(qiáng)處理,增加圖像的對(duì)比度,用Lee濾波對(duì)SAR圖像進(jìn)行濾波處理。實(shí)驗(yàn)結(jié)果表明,這種圖像預(yù)處理方法處理后的SAR圖像,可以取得很好的檢測(cè)效果。實(shí)驗(yàn)檢測(cè)數(shù)據(jù)表明,文中方法不僅能有效的檢測(cè)出SAR圖像中的海岸線,而且與其他相關(guān)海岸線檢測(cè)方法相比,其迭代次數(shù)減少了、迭代時(shí)間縮短了而且檢測(cè)準(zhǔn)確度得到了進(jìn)一步提升,顯示出該方法的有效性。
[Abstract]:Coastline detection in synthetic Aperture Radar (Synthetic Aperture Radar,SAR) images plays an important role in coastline management, map automatic navigation, ship target recognition and so on. Geometric active contour (Geometric Active Contour,GAC) model is developed on the basis of active contour (Active Contour Model,ACM, (also called Snake model) model. It is an important breakthrough in the field of extracting image boundary, and has a very practical research value. In recent years, with the extensive and in-depth study of the Snake model, the idea of the GAC model has been paid more and more attention in the world, and the domain involved in the field is also more and more extensive. The GAC model has also shown great practicability in the field of extracting the SAR image boundary. However, there are still some weak boundary problems in SAR image processing by using GAC model because of the problems of blurry boundary, low contrast, many grayscale levels and easy to be disturbed by noise. The number of iterations and the time of iteration are easily affected by the initial contour of the image and the influence of image preprocessing on extracting the coastline of SAR image. In order to solve this problem, this paper takes coastline detection of SAR image as the application background, and discusses the weak boundary problem involved in it. The effects of image initial contour on the number of iterations and iterative time of shoreline detection and the effect of image preprocessing on coastline detection are systematically studied. After studying the characteristic of detecting weak boundary of coastline in SAR image, the improved symbolic pressure function combined with regional information is proposed as the boundary stopping condition of GAC model and the coastline is extracted accurately. This method can make up for the weak boundary of coastline in SAR image and make the extracted coastline more accurate. On the basis of the research on the weak boundary of the shoreline extracted from SAR image, the selection of initial contour of SAR image and the influence of image preprocessing on the extracted coastline are also studied in this paper. In this paper, we will study the influence of the initial contour of SAR image on the number of iterations and the iteration time of coastline detection, which is based on the GAC model which is not sensitive to the initial contour of the image. The larger the initial contour selection of the image, the less the number of iterations and the shorter the iteration time of the GAC model. In image preprocessing, because speckle noise in SAR image is multiplicative, the general image enhancement method and noise removal method are no longer suitable for SAR image. In this paper, the method of gray level transformation is used to enhance SAR image. The contrast of the image is increased, and the SAR image is filtered by Lee filter. The experimental results show that the SAR images processed by this method can achieve good detection results. Experimental data show that the proposed method can not only effectively detect the coastline in SAR images, but also reduce the number of iterations compared with other relevant shoreline detection methods. The iteration time is shortened and the detection accuracy is further improved, which shows the effectiveness of the method.
【學(xué)位授予單位】：江蘇科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TN957.52
本文編號(hào)：2238161
[Abstract]:Coastline detection in synthetic Aperture Radar (Synthetic Aperture Radar,SAR) images plays an important role in coastline management, map automatic navigation, ship target recognition and so on. Geometric active contour (Geometric Active Contour,GAC) model is developed on the basis of active contour (Active Contour Model,ACM, (also called Snake model) model. It is an important breakthrough in the field of extracting image boundary, and has a very practical research value. In recent years, with the extensive and in-depth study of the Snake model, the idea of the GAC model has been paid more and more attention in the world, and the domain involved in the field is also more and more extensive. The GAC model has also shown great practicability in the field of extracting the SAR image boundary. However, there are still some weak boundary problems in SAR image processing by using GAC model because of the problems of blurry boundary, low contrast, many grayscale levels and easy to be disturbed by noise. The number of iterations and the time of iteration are easily affected by the initial contour of the image and the influence of image preprocessing on extracting the coastline of SAR image. In order to solve this problem, this paper takes coastline detection of SAR image as the application background, and discusses the weak boundary problem involved in it. The effects of image initial contour on the number of iterations and iterative time of shoreline detection and the effect of image preprocessing on coastline detection are systematically studied. After studying the characteristic of detecting weak boundary of coastline in SAR image, the improved symbolic pressure function combined with regional information is proposed as the boundary stopping condition of GAC model and the coastline is extracted accurately. This method can make up for the weak boundary of coastline in SAR image and make the extracted coastline more accurate. On the basis of the research on the weak boundary of the shoreline extracted from SAR image, the selection of initial contour of SAR image and the influence of image preprocessing on the extracted coastline are also studied in this paper. In this paper, we will study the influence of the initial contour of SAR image on the number of iterations and the iteration time of coastline detection, which is based on the GAC model which is not sensitive to the initial contour of the image. The larger the initial contour selection of the image, the less the number of iterations and the shorter the iteration time of the GAC model. In image preprocessing, because speckle noise in SAR image is multiplicative, the general image enhancement method and noise removal method are no longer suitable for SAR image. In this paper, the method of gray level transformation is used to enhance SAR image. The contrast of the image is increased, and the SAR image is filtered by Lee filter. The experimental results show that the SAR images processed by this method can achieve good detection results. Experimental data show that the proposed method can not only effectively detect the coastline in SAR images, but also reduce the number of iterations compared with other relevant shoreline detection methods. The iteration time is shortened and the detection accuracy is further improved, which shows the effectiveness of the method.
【學(xué)位授予單位】：江蘇科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TN957.52
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