基于水平集的SAR遙感圖像分割的算法研究
發(fā)布時間：2018-07-26 13:23
【摘要】：合成孔徑雷達(SAR)是一種高分辨的微波遙感相干成像雷達,在軍事和國民經(jīng)濟等各個領(lǐng)域中都有著非常重要的作用。SAR遙感圖像的分割是進行SAR遙感圖像理解、解疑中基本且關(guān)鍵的技術(shù)之一。SAR遙感圖像分割的目的就是把目標區(qū)域和背景區(qū)域分割開來,但由于SAR遙感圖像中含有大量乘性相干斑噪聲,且圖像區(qū)域灰度分布不均勻,使得SAR遙感圖像中目標物體邊緣無法被精確定位,進而很難實現(xiàn)對SAR遙感圖像精確且高效率的分割。如何快速而有效地實現(xiàn)SAR遙感圖像的分割,是目前亟待解決的一個難題。隨著SAR遙感圖像研究的發(fā)展,水平集模型以其對曲線拓撲結(jié)構(gòu)變化的良好適應(yīng)能力和無需對噪聲預(yù)處理的特性,受到國內(nèi)外研究學(xué)者們的青睞。本文在總結(jié)和分析已有的基于水平集的SAR遙感圖像分割方法的基礎(chǔ)上,針對SAR遙感圖像所具有的大量乘性相干斑噪聲和灰度分布不均勻的特性,提出了兩種融合區(qū)域信息和邊緣梯度信息的水平集模型,對SAR遙感圖像進行分割,主要工作如下:針對SAR遙感圖像中目標邊緣模糊和對目標邊緣定位不正確的問題,提出了一種基于改進C-V模型的高分辨率SAR遙感圖像的分割方法。該方法針對C-V模型不能分割灰度不均勻圖像的缺點,以及該模型只利用區(qū)域信息而沒有利用邊緣梯度信息,從而造成分割后的目標物體虛假邊緣較多的缺點,本文利用SAR遙感圖像所特有的統(tǒng)計特性,提出了利用對均勻和不均勻區(qū)域都有很好擬合作用的G0分布函數(shù),對圖像進行擬合,解決對灰度分布不均勻圖像分割不準確的問題,同時在C-V模型中引入改進的邊緣指示函數(shù),此邊緣指示函數(shù)能夠很好地去除SAR遙感圖像中具有的乘性噪聲、定位目標的邊界、控制曲線的演化速率以及避免水平集函數(shù)的重新初始化。針對SAR遙感圖像存在的灰度分布不均勻現(xiàn)象,提出了一種基于改進LIF模型的SAR遙感圖像的分割方法。該方法是在LIF模型能較好地分割灰度不均勻圖像的基礎(chǔ)上,針對局部圖像擬合(LIF)模型存在的對噪聲敏感,以及在演化過程中易陷入局部極小值和邊緣定位不準確的缺點,引入截斷的基于線性最小均方誤差的指數(shù)平滑濾波器來提高分割精度,同時引入結(jié)合了模糊C均值(FCM)和無限對指數(shù)濾波器的,基于梯度信息和全局區(qū)域信息的邊緣檢測函數(shù),來避免陷入局部最優(yōu)和邊界定位不準的問題。利用人工合成的圖像和真實的道路、湖泊以及艦船的高分辨率SAR遙感圖像進行分割實驗,對比已有的基于水平集的SAR遙感圖像分割方法,證明了本文的兩種改進水平集方法都能夠在背景雜波下,很好地抑制乘性相干斑噪聲,準確地定位目標物體的邊緣輪廓,提高對SAR遙感圖像的分割精度。
[Abstract]:Synthetic Aperture Radar (SAR) is a kind of high-resolution microwave remote sensing coherent imaging radar, which plays an important role in military and national economy. One of the basic and key techniques of SAR remote sensing image segmentation is to separate the target region from the background area. However, there are a lot of multiplicative speckle noises in the SAR remote sensing image and the gray distribution of the image region is not uniform. The edge of object in SAR remote sensing image can not be accurately located, and it is difficult to segment SAR image accurately and efficiently. How to quickly and effectively realize the segmentation of SAR remote sensing image is a difficult problem to be solved. With the development of SAR remote sensing image research, the level set model is favored by researchers at home and abroad because of its good adaptability to the curve topology change and no need for noise preprocessing. On the basis of summarizing and analyzing the existing SAR remote sensing image segmentation methods based on level set, this paper aims at the multiplicative speckle noise and uneven gray distribution of SAR remote sensing image. In this paper, two level set models for fusion of regional information and edge gradient information are proposed. The main work of segmentation of SAR remote sensing image is as follows: aiming at the problem of target edge blur and target edge location incorrectly in SAR remote sensing image, A high resolution SAR remote sensing image segmentation method based on improved C-V model is proposed. This method aims at the disadvantage that C-V model can not segment uneven grayscale image, and the model only uses the region information but not the edge gradient information, which results in more false edges of the target object after segmentation. In this paper, based on the statistical characteristics of SAR remote sensing images, a G0 distribution function, which can fit both uniform and non-uniform regions, is proposed to fit the images and to solve the problem of inaccurate segmentation of non-uniform gray-scale images. At the same time, an improved edge indicator function is introduced into the C-V model. The edge indicator function can remove the multiplicative noise in the SAR remote sensing image and locate the boundary of the target. The evolution rate of the control curve and the reinitialization of the level set function are avoided. Aiming at the uneven gray distribution of SAR remote sensing images, a method of SAR remote sensing image segmentation based on improved LIF model is proposed. This method is based on the fact that the LIF model can segment inhomogeneous grayscale images well, and aims at the disadvantages of local image fitting (LIF) model, which is sensitive to noise and easy to fall into local minimum value and inaccurate edge location in the evolution process. The truncated exponential smoothing filter based on linear minimum mean square error is introduced to improve the segmentation accuracy, and the edge detection function based on gradient information and global region information is introduced, which combines fuzzy C-means (FCM) and infinite pair exponential filter. To avoid the problem of local optimum and inaccurate boundary location. Using artificial synthetic image and real road, lake and ship high resolution SAR remote sensing image segmentation experiment, compare the existing SAR remote sensing image segmentation method based on level set. It is proved that the two improved level set methods in this paper can effectively suppress multiplicative speckle noise under background clutter, accurately locate the edge contour of the target object, and improve the segmentation accuracy of SAR remote sensing images.
【學(xué)位授予單位】：江蘇科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP751
本文編號：2146147
[Abstract]:Synthetic Aperture Radar (SAR) is a kind of high-resolution microwave remote sensing coherent imaging radar, which plays an important role in military and national economy. One of the basic and key techniques of SAR remote sensing image segmentation is to separate the target region from the background area. However, there are a lot of multiplicative speckle noises in the SAR remote sensing image and the gray distribution of the image region is not uniform. The edge of object in SAR remote sensing image can not be accurately located, and it is difficult to segment SAR image accurately and efficiently. How to quickly and effectively realize the segmentation of SAR remote sensing image is a difficult problem to be solved. With the development of SAR remote sensing image research, the level set model is favored by researchers at home and abroad because of its good adaptability to the curve topology change and no need for noise preprocessing. On the basis of summarizing and analyzing the existing SAR remote sensing image segmentation methods based on level set, this paper aims at the multiplicative speckle noise and uneven gray distribution of SAR remote sensing image. In this paper, two level set models for fusion of regional information and edge gradient information are proposed. The main work of segmentation of SAR remote sensing image is as follows: aiming at the problem of target edge blur and target edge location incorrectly in SAR remote sensing image, A high resolution SAR remote sensing image segmentation method based on improved C-V model is proposed. This method aims at the disadvantage that C-V model can not segment uneven grayscale image, and the model only uses the region information but not the edge gradient information, which results in more false edges of the target object after segmentation. In this paper, based on the statistical characteristics of SAR remote sensing images, a G0 distribution function, which can fit both uniform and non-uniform regions, is proposed to fit the images and to solve the problem of inaccurate segmentation of non-uniform gray-scale images. At the same time, an improved edge indicator function is introduced into the C-V model. The edge indicator function can remove the multiplicative noise in the SAR remote sensing image and locate the boundary of the target. The evolution rate of the control curve and the reinitialization of the level set function are avoided. Aiming at the uneven gray distribution of SAR remote sensing images, a method of SAR remote sensing image segmentation based on improved LIF model is proposed. This method is based on the fact that the LIF model can segment inhomogeneous grayscale images well, and aims at the disadvantages of local image fitting (LIF) model, which is sensitive to noise and easy to fall into local minimum value and inaccurate edge location in the evolution process. The truncated exponential smoothing filter based on linear minimum mean square error is introduced to improve the segmentation accuracy, and the edge detection function based on gradient information and global region information is introduced, which combines fuzzy C-means (FCM) and infinite pair exponential filter. To avoid the problem of local optimum and inaccurate boundary location. Using artificial synthetic image and real road, lake and ship high resolution SAR remote sensing image segmentation experiment, compare the existing SAR remote sensing image segmentation method based on level set. It is proved that the two improved level set methods in this paper can effectively suppress multiplicative speckle noise under background clutter, accurately locate the edge contour of the target object, and improve the segmentation accuracy of SAR remote sensing images.
【學(xué)位授予單位】：江蘇科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP751
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