基于幾何活動輪廓模型的SAR圖像海岸線檢測
發(fā)布時間：2018-09-12 06:20
【摘要】：合成孔徑雷達(Synthetic Aperture Radar,SAR)圖像海岸線檢測在海岸線管理、地圖自動導航、船艦目標識別等方面發(fā)揮著重要的作用。幾何活動輪廓(Geometric Active Contour,GAC)模型是在活動輪廓(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,而且有非常實用的研究價值。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)域上也顯示出強大的實用性。但是由于SAR圖像具有邊界模糊、對比度小、灰度等級多并且易受噪聲干擾等問題,GAC模型的方法處理SAR圖像仍然會遇到一些弱邊界問題、迭代次數(shù)和迭代時間易受圖像初始輪廓影響以及圖像預處理對提取SAR圖像的海岸線造成影響的問題。針對此問題,本文以SAR圖像海岸線檢測為應用背景,對其中涉及的弱邊界問題、圖像初始輪廓影響海岸線檢測的迭代次數(shù)和迭代時間的問題及圖像預處理對海岸線檢測的影響進行了系統(tǒng)研究。經(jīng)過研究SAR圖像海岸線檢測弱邊界的特點,提出利用結(jié)合區(qū)域信息的改進符號壓力函數(shù)為GAC模型的邊界停止條件并對海岸線進行精確提取,這樣能很好的彌補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確。在提取SAR圖像海岸線存在弱邊界問題進行研究的基礎(chǔ)上,本文也對SAR圖像初始輪廓的選取、圖像預處理對提取出的海岸線的影響進行了研究。本文將研究SAR圖像不同大小的初始輪廓對海岸線檢測的迭代次數(shù)及迭代時間的影響,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)越少則迭代時間越短。在圖像的預處理中,因為SAR圖像中的斑點噪聲是乘性的,所以一般的圖像增強方法、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SAR圖像進行增強處理,增加圖像的對比度,用Lee濾波對SAR圖像進行濾波處理。實驗結(jié)果表明,這種圖像預處理方法處理后的SAR圖像,可以取得很好的檢測效果。實驗檢測數(shù)據(jù)表明,文中方法不僅能有效的檢測出SAR圖像中的海岸線,而且與其他相關(guān)海岸線檢測方法相比,其迭代次數(shù)減少了、迭代時間縮短了而且檢測準確度得到了進一步提升,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.
【學位授予單位】：江蘇科技大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TN957.52
本文編號：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.
【學位授予單位】：江蘇科技大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TN957.52
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