【摘要】：合成孔徑雷達(dá)(Synthetic Aperture Radar,SAR)圖像分割,是SAR圖像處理中的基礎(chǔ)且關(guān)鍵步驟。由于相干斑噪聲的影響,對SAR圖像的分割,需要根據(jù)SAR圖像所特有的特征來完成�；谧兎掷碚摰腟AR圖像分割,可以根據(jù)不同的SAR圖像特征建立不同的能量泛函,并利用變分法最小化能量泛函,實(shí)現(xiàn)SAR圖像的分割。本文就變分SAR圖像分割技術(shù)展開了研究,主要工作如下:(1)研究了利用邊界信息的GAC模型和利用區(qū)域信息的CV模型,并結(jié)合邊界信息和區(qū)域信息,建立了多區(qū)域的SAR圖像分割模型。針對水平集方法數(shù)值求解耗時(shí)的問題,在Potts模型的基礎(chǔ)上推導(dǎo)出了平滑對偶模型,給出了一種基于對偶的快速算法。在SAR圖像分割的速度和精確度上,與水平集方法進(jìn)行了對比,用實(shí)驗(yàn)驗(yàn)證了對偶算法的有效性和快速性。(2)分析了均勻SAR圖像的Gamma統(tǒng)計(jì)分布特征,以及非均勻SAR圖像的紋理特征。利用灰度共生矩陣(GLCM)提取SAR圖像的紋理特征,得到紋理特征向量,與Gamma分布統(tǒng)計(jì)特征進(jìn)行結(jié)合,并利用Potts模型建立了能量泛函。將SAR圖像分割結(jié)果,與單獨(dú)使用統(tǒng)計(jì)特征或者紋理特征進(jìn)行對比,特征結(jié)合得到的分割結(jié)果更加精確。(3)針對極化SAR圖像特有的性質(zhì),利用極化相干矩陣的復(fù)Wishart分布和Potts模型建立能量泛函。對偶算法進(jìn)行最小化時(shí),在H??分類的基礎(chǔ)上,利用復(fù)Wishart分布實(shí)現(xiàn)了極化SAR圖像的自動初始化,可以自動確定初始化曲線和分類數(shù)目,考慮了極化SAR圖像的統(tǒng)計(jì)特征和散射特征。與隨機(jī)初始化和人工初始化的極化SAR圖像分割結(jié)果進(jìn)行了對比,自動初始化分割結(jié)果更符合真實(shí)的地物信息。
[Abstract]:Synthetic Aperture Radar (Synthetic Aperture Radar,SAR) image segmentation is the basis and key step in SAR image processing. Due to the influence of speckle noise, the segmentation of SAR image needs to be completed according to the characteristics of SAR image. The SAR image segmentation based on variation theory can establish different energy Functionals according to different SAR image features, and use the variation method to minimize the energy Functionals to realize the segmentation of SAR images. In this paper, the variation SAR image segmentation technology is studied, the main work is as follows: (1) the GAC model using boundary information and the CV model using region information are studied, and the boundary information and region information are combined. A multi-region SAR image segmentation model is established. In order to solve the time-consuming problem of level set method, a smooth dual model is derived on the basis of Potts model, and a fast algorithm based on duality is given. The speed and accuracy of SAR image segmentation are compared with the level set method, and the effectiveness and rapidity of the dual algorithm are verified by experiments. (2) the Gamma statistical distribution characteristics of uniform SAR images are analyzed. And the texture features of non-uniform SAR images. The gray co-occurrence matrix (GLCM) is used to extract the texture features of SAR images, and the texture feature vectors are obtained, which are combined with the statistical features of Gamma distribution, and the energy functional is established by using Potts model. The segmentation results of SAR images are compared with those of statistical features or texture features alone, and the segmentation results obtained by the combination of features are more accurate. (3) aiming at the unique properties of polarized SAR images, The energy functional is established by using the complex Wishart distribution of polarization coherence matrix and Potts model. When the dual algorithm is minimized, in H? On the basis of classification, the automatic initialization of polarized SAR image is realized by using complex Wishart distribution, and the initialization curve and classification number can be determined automatically, and the statistical and scattering characteristics of polarized SAR image are considered. Compared with the polarization SAR image segmentation results of random initialization and manual initialization, the automatic initialization segmentation results are more in line with the real ground object information.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TN957.52

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相關(guān)期刊論文 前1條

1 東野長磊;鄭永果;姜東煥;張彬;;基于全局極小解Chan-Vese模型的SAR圖像分割[J];計(jì)算機(jī)工程與設(shè)計(jì);2012年11期

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本文編號：2474183