基于快速區(qū)域合并的SAR圖像分割算法
發(fā)布時間：2019-06-07 13:13
【摘要】：合成孔徑雷達(dá)(synthetic aperture radar,SAR)成像系統(tǒng)已經(jīng)被廣泛應(yīng)用,如目標(biāo)檢測與識別、海洋監(jiān)視、地形繪制和自然災(zāi)情監(jiān)測等。SAR圖像分割是SAR圖像信息提取和自動理解的一個重要問題,它通過將一幅SAR圖像分割成互不重疊的同質(zhì)區(qū)域來提取場景的結(jié)構(gòu)信息。SAR的相干成像原理使得SAR圖像中存在大量隨機(jī)分布的相干斑噪聲,這些相干斑噪聲降低了SAR圖像的質(zhì)量,同時,增加了SAR圖像分割的難度。本論文主要研究SAR圖像分割問題的模型建立及其優(yōu)化求解問題。提出幾種基于區(qū)域合并技術(shù)的SAR圖像分割算法。它們是:1.邊緣信息引導(dǎo)區(qū)域合并SAR圖像分割算法。針對基于區(qū)域合并技術(shù)的SAR圖像分割算法中區(qū)域合并的順序問題,提出一種由邊緣信息引導(dǎo)的區(qū)域合并技術(shù)。首先,利用多方向比例邊緣檢測算子提取SAR圖像的比例邊緣強(qiáng)度映射(ratio edge strength map,RESM),提出一種新的閾值處理方法來抑制RESM的均質(zhì)區(qū)域內(nèi)部的極小值,進(jìn)而減少了對閾值處理后的RESM進(jìn)行分水嶺變換獲得的初始分割的區(qū)域個數(shù)。然后,利用相鄰區(qū)域的面積和邊緣信息設(shè)計一個區(qū)域合并優(yōu)先級函數(shù)來引導(dǎo)區(qū)域合并的進(jìn)行,該方法提高了模型參數(shù)的估計精度,同時保留圖像的強(qiáng)邊緣;最后,將邊緣信息引導(dǎo)區(qū)域合并技術(shù)用于求解基于多邊形網(wǎng)格和最短描述長度(minimum description length,MDL)準(zhǔn)則的SAR圖像分割模型。該方法提高了分割結(jié)果中區(qū)域邊緣的定位能力與定位精度。2.基于網(wǎng)格編碼和區(qū)域合并的MDL準(zhǔn)則SAR圖像分割算法。建立一種新的基于八鄰域鏈碼網(wǎng)格編碼和MDL準(zhǔn)則的SAR圖像分割模型,并用區(qū)域合并技術(shù)實現(xiàn)模型的快速優(yōu)化求解。結(jié)合比例邊緣檢測算子和分水嶺變換獲得SAR圖像的初始過分割結(jié)果;遞歸地合并使分割模型減低最快的相鄰區(qū)域?qū)崿F(xiàn)分割模型的優(yōu)化求解。利用區(qū)域鄰接圖(region adjacency graph,RAG)及其最近鄰圖(nearest neighbor graph,NNG)特性來加速區(qū)域合并過程。利用數(shù)值指標(biāo)精確度(P)和命中率(R)來評價分割算法的邊緣定位能力。實驗結(jié)果表明,該方法具有高的邊緣定位能力和低的時間復(fù)雜度。3.基于G0分布和鏈碼網(wǎng)格的SAR圖像分割算法。為了降低SAR圖像的場景復(fù)雜度對其分割結(jié)果的影響,提出一種基于MDL準(zhǔn)則的自適應(yīng)權(quán)值SAR圖像分割模型。該模型利用G0分布描述SAR圖像數(shù)據(jù),用鏈碼網(wǎng)格對SAR圖像中區(qū)域的邊緣進(jìn)行編碼。提出一種利用SAR圖像數(shù)據(jù)自適應(yīng)地估計分割模型的權(quán)值的方法。遞歸地合并初始分割結(jié)果中使分割模型降低最快的相鄰區(qū)域?qū)崿F(xiàn)模型快速優(yōu)化求解。實驗結(jié)果表明,該方法有效地減輕了紋理區(qū)域的過分割程度。4.邊緣懲罰分層區(qū)域合并SAR圖像分割算法。利用方向邊緣強(qiáng)度信息,建立一種新的邊緣懲罰SAR圖像分割模型,提出一種最小化該模型的分層區(qū)域合并算法。利用多方向比例邊緣檢測算子提取SAR圖像的邊緣強(qiáng)度信息,并結(jié)合分水嶺變換獲得SAR圖像的高質(zhì)量的初始過分割結(jié)果。利用多邊形近似區(qū)域邊緣,提取邊緣的方向,將方向邊緣強(qiáng)度映射融入邊緣懲罰中,獲得懲罰強(qiáng)度與邊緣強(qiáng)度成反比的邊緣懲罰項。逐漸增大邊緣懲罰項的強(qiáng)度,獲得由圖像特征驅(qū)動的分層區(qū)域合并算法。利用RAG表示圖像分割結(jié)果,加速區(qū)域合并。實驗結(jié)果表明:該方法與其它方法相比在性能和效率上都有優(yōu)勢,獲得更好的分割結(jié)果。5.相對公共邊界長度懲罰區(qū)域合并SAR圖像分割算法。提出一種基于區(qū)域合并技術(shù)的快速SAR圖像分割算法。該算法對閾值處理后的比例邊緣強(qiáng)度映射進(jìn)行分水嶺變換實現(xiàn)SAR圖像的快速初始過分割,利用提出的基于相對公共邊界長度懲罰的區(qū)域合并代價和用于快速搜索初始分割中最小權(quán)值相鄰區(qū)域的NNG實現(xiàn)快速區(qū)域合并。提出一種新的度量相鄰區(qū)域之間的相似性的統(tǒng)計相似性度量,該度量具有尺度不變性和對區(qū)域尺寸的近似恒虛警特性,將該統(tǒng)計相似性度量與提出的相對公共邊界長度懲罰項結(jié)合,得到新的區(qū)域合并代價。利用RAG和NNG加速區(qū)域合并過程。利用數(shù)值指標(biāo)精確度(P)和命中率(R)來度量最終分割結(jié)果的邊緣定位能力,區(qū)域覆蓋準(zhǔn)則度量最終分割結(jié)果的區(qū)域檢測性能。通過合成的和真實的SAR圖像分割試驗表明,與兩種經(jīng)典的SAR圖像分割算法相比,該算法在效率和性能上均具有優(yōu)勢。
[Abstract]:Synthetic Aperture Radar (SAR) imaging systems have been widely used, such as target detection and identification, ocean surveillance, terrain rendering and natural disaster monitoring. SAR image segmentation is an important problem for SAR image information extraction and automatic understanding. It extracts the structure information of the scene by dividing a SAR image into a homogeneous region that does not overlap each other. The coherent imaging principle of SAR has a large number of coherent speckle noise in the SAR image, which reduces the quality of the SAR image, and increases the difficulty of the SAR image segmentation. This paper mainly studies the model establishment of SAR image segmentation and its optimization solution. In this paper, several SAR image segmentation algorithms based on region merging technology are proposed. They are:1. The edge information guide area combines the SAR image segmentation algorithm. In order to solve the order problem of the region merging in the SAR image segmentation algorithm based on the region merging technology, an area merging technique guided by the edge information is proposed. First, the ratio edge strength map (RESM) of the SAR image is extracted by the multi-directional proportional edge detection operator, a new threshold processing method is proposed to suppress the minimum value inside the homogeneous region of the RESM, And further, the number of areas of the initial segmentation obtained by the watershed transformation of the RESM after the threshold processing is reduced. then, using the area of the adjacent area and the edge information to design a region combined priority function to guide the implementation of the region combination, The edge information guidance area merging technique is used to solve the SAR image segmentation model based on the polygon mesh and the shortest description length (MDL) criterion. The method improves the positioning capability and the positioning precision of the area edge in the segmentation result. The image segmentation algorithm of the MDL based on the mesh coding and the region merging is presented. A new SAR image segmentation model based on eight-neighborhood chain code mesh coding and MDL criterion is set up, and the rapid optimization solution of the model is realized by the region merging technique. The initial over-segmentation result of the SAR image is obtained by combining the proportional edge detection operator and the watershed transform, and the optimization solution of the segmentation model is realized by recursively combining the adjacent regions with the fastest reduction in the segmentation model. The region merging process is accelerated using the region adjacency graph (rag) and its nearest neighbor graph (nng) characteristics. The edge positioning ability of the segmentation algorithm is evaluated by the numerical index precision (P) and the hit rate (R). The experimental results show that the method has high edge positioning ability and low time complexity. The algorithm of SAR image segmentation based on the G0 distribution and the chain code grid is presented. In order to reduce the influence of the scene complexity of the SAR image on the segmentation result, an adaptive weight SAR image segmentation model based on the MDL criterion is proposed. The model uses the G0 distribution to describe the SAR image data, and uses the chain code mesh to encode the edge of the region in the SAR image. A method for adaptively estimating the weight of a segmentation model using SAR image data is presented. And recursively combining the initial segmentation results to enable the segmentation model to reduce the fastest adjacent region to realize the rapid optimization solution of the model. The experimental results show that the method can effectively reduce the degree of oversegmentation of the texture region. The edge penalty layered region is combined with the SAR image segmentation algorithm. In this paper, a new edge penalty SAR image segmentation model is set up by using the direction edge strength information, and a layered region combining algorithm is proposed to minimize the model. The edge intensity information of the SAR image is extracted by the multi-directional proportional edge detection operator, and the high-quality initial over-segmentation result of the SAR image is obtained by combining the watershed transform. By using the edge of the polygonal approximation area, the direction of the edge is extracted, and the edge intensity of the direction is mapped into the edge penalty to obtain an edge penalty term which is inversely proportional to the edge intensity. The intensity of the edge penalty term is gradually increased to obtain a layered region combining algorithm driven by the image feature. The image segmentation result is represented by the RAG, and the acceleration region is combined. The experimental results show that the method is superior to other methods in terms of performance and efficiency, and a better segmentation result is obtained. A SAR image segmentation algorithm is combined with respect to the common boundary length penalty area. A fast SAR image segmentation algorithm based on region merging technology is proposed. the algorithm performs the watershed transform on the scale edge intensity mapping after the threshold processing to realize the fast initial over-segmentation of the SAR image, And fast region merging is realized by utilizing the proposed region combining cost based on the relative common boundary length penalty and the NNG used for fast searching the minimum weight adjacent area in the initial segmentation. A new statistical similarity measure of similarity between adjacent regions is proposed, which has a scale invariance and an approximate constant false alarm characteristic for the size of the region, and the statistical similarity measure is combined with the proposed relative common boundary length penalty term to obtain a new region combining cost. The region merging process is accelerated by the rag and the nng. The edge positioning ability of the final segmentation result is measured by the numerical index accuracy (P) and the hit rate (R), and the region coverage criterion measures the region detection performance of the final segmentation result. The synthetic and real SAR image segmentation test shows that the algorithm has advantages in both efficiency and performance compared to the two classical SAR image segmentation algorithms.
【學(xué)位授予單位】：西安電子科技大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2014
【分類號】：TN957.52
本文編號：2494822
[Abstract]:Synthetic Aperture Radar (SAR) imaging systems have been widely used, such as target detection and identification, ocean surveillance, terrain rendering and natural disaster monitoring. SAR image segmentation is an important problem for SAR image information extraction and automatic understanding. It extracts the structure information of the scene by dividing a SAR image into a homogeneous region that does not overlap each other. The coherent imaging principle of SAR has a large number of coherent speckle noise in the SAR image, which reduces the quality of the SAR image, and increases the difficulty of the SAR image segmentation. This paper mainly studies the model establishment of SAR image segmentation and its optimization solution. In this paper, several SAR image segmentation algorithms based on region merging technology are proposed. They are:1. The edge information guide area combines the SAR image segmentation algorithm. In order to solve the order problem of the region merging in the SAR image segmentation algorithm based on the region merging technology, an area merging technique guided by the edge information is proposed. First, the ratio edge strength map (RESM) of the SAR image is extracted by the multi-directional proportional edge detection operator, a new threshold processing method is proposed to suppress the minimum value inside the homogeneous region of the RESM, And further, the number of areas of the initial segmentation obtained by the watershed transformation of the RESM after the threshold processing is reduced. then, using the area of the adjacent area and the edge information to design a region combined priority function to guide the implementation of the region combination, The edge information guidance area merging technique is used to solve the SAR image segmentation model based on the polygon mesh and the shortest description length (MDL) criterion. The method improves the positioning capability and the positioning precision of the area edge in the segmentation result. The image segmentation algorithm of the MDL based on the mesh coding and the region merging is presented. A new SAR image segmentation model based on eight-neighborhood chain code mesh coding and MDL criterion is set up, and the rapid optimization solution of the model is realized by the region merging technique. The initial over-segmentation result of the SAR image is obtained by combining the proportional edge detection operator and the watershed transform, and the optimization solution of the segmentation model is realized by recursively combining the adjacent regions with the fastest reduction in the segmentation model. The region merging process is accelerated using the region adjacency graph (rag) and its nearest neighbor graph (nng) characteristics. The edge positioning ability of the segmentation algorithm is evaluated by the numerical index precision (P) and the hit rate (R). The experimental results show that the method has high edge positioning ability and low time complexity. The algorithm of SAR image segmentation based on the G0 distribution and the chain code grid is presented. In order to reduce the influence of the scene complexity of the SAR image on the segmentation result, an adaptive weight SAR image segmentation model based on the MDL criterion is proposed. The model uses the G0 distribution to describe the SAR image data, and uses the chain code mesh to encode the edge of the region in the SAR image. A method for adaptively estimating the weight of a segmentation model using SAR image data is presented. And recursively combining the initial segmentation results to enable the segmentation model to reduce the fastest adjacent region to realize the rapid optimization solution of the model. The experimental results show that the method can effectively reduce the degree of oversegmentation of the texture region. The edge penalty layered region is combined with the SAR image segmentation algorithm. In this paper, a new edge penalty SAR image segmentation model is set up by using the direction edge strength information, and a layered region combining algorithm is proposed to minimize the model. The edge intensity information of the SAR image is extracted by the multi-directional proportional edge detection operator, and the high-quality initial over-segmentation result of the SAR image is obtained by combining the watershed transform. By using the edge of the polygonal approximation area, the direction of the edge is extracted, and the edge intensity of the direction is mapped into the edge penalty to obtain an edge penalty term which is inversely proportional to the edge intensity. The intensity of the edge penalty term is gradually increased to obtain a layered region combining algorithm driven by the image feature. The image segmentation result is represented by the RAG, and the acceleration region is combined. The experimental results show that the method is superior to other methods in terms of performance and efficiency, and a better segmentation result is obtained. A SAR image segmentation algorithm is combined with respect to the common boundary length penalty area. A fast SAR image segmentation algorithm based on region merging technology is proposed. the algorithm performs the watershed transform on the scale edge intensity mapping after the threshold processing to realize the fast initial over-segmentation of the SAR image, And fast region merging is realized by utilizing the proposed region combining cost based on the relative common boundary length penalty and the NNG used for fast searching the minimum weight adjacent area in the initial segmentation. A new statistical similarity measure of similarity between adjacent regions is proposed, which has a scale invariance and an approximate constant false alarm characteristic for the size of the region, and the statistical similarity measure is combined with the proposed relative common boundary length penalty term to obtain a new region combining cost. The region merging process is accelerated by the rag and the nng. The edge positioning ability of the final segmentation result is measured by the numerical index accuracy (P) and the hit rate (R), and the region coverage criterion measures the region detection performance of the final segmentation result. The synthetic and real SAR image segmentation test shows that the algorithm has advantages in both efficiency and performance compared to the two classical SAR image segmentation algorithms.
【學(xué)位授予單位】：西安電子科技大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2014
【分類號】：TN957.52
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