本文選題：極化SAR　切入點(diǎn)：建筑物檢測(cè)　出處：《電子科技大學(xué)》2017年碩士論文

【摘要】：利用極化合成孔徑雷達(dá)(PolSAR)影像進(jìn)行地物目標(biāo)檢測(cè)和識(shí)別是當(dāng)今極化SAR解譯的重要研究課題,從極化SAR影像中檢測(cè)出建筑物對(duì)于土地利用調(diào)查、城市規(guī)劃以及城市變化監(jiān)測(cè)等應(yīng)用具有重要的理論意義和實(shí)用價(jià)值。然而由于建筑物具有比較復(fù)雜的幾何結(jié)構(gòu)分布,并且建筑物的交叉極化散射往往容易導(dǎo)致其被誤分為森林等其它地物,因此從極化SAR影像中檢測(cè)建筑物仍然是一個(gè)具有挑戰(zhàn)性的課題。本文利用高分辨率極化SAR影像含有的幾何特征、紋理特征和極化散射特征等屬性來(lái)檢測(cè)建筑物,將建筑物的紋理特征與其極化散射特征相結(jié)合,從極化SAR影像中檢測(cè)出建筑物。本文以L波段星載ALOS-2 PALSAR-2全極化SAR影像和機(jī)載E-SAR全極化SAR影像作為實(shí)驗(yàn)數(shù)據(jù),分別利用極化SAR影像中建筑物的幾何特征、紋理特征和極化散射特征來(lái)檢測(cè)建筑物,并且通過(guò)加權(quán)特征融合方法將紋理特征和極化散射特征等多種特征相結(jié)合實(shí)現(xiàn)建筑物的檢測(cè),主要的研究工作和結(jié)論如下:(1)利用基于高分辨率極化SAR影像建筑物幾何特征的標(biāo)記分水嶺變換方法檢測(cè)建筑物。建筑物的邊緣輪廓信息是其在極化SAR影像中所呈現(xiàn)出的主要的幾何特征,利用基于建筑物幾何特征的標(biāo)記分水嶺變換方法從高分辨率極化SAR影像中檢測(cè)出建筑物,且實(shí)驗(yàn)結(jié)果表明該方法得到的檢測(cè)結(jié)果對(duì)建筑物邊緣輪廓信息有較好的保持度。(2)基于極化散射特征對(duì)全極化SAR影像進(jìn)行建筑物檢測(cè)。本文中建筑物的極化散射特征主要包括極化目標(biāo)分解參數(shù)和圓極化相關(guān)系數(shù)。主要利用幾種常用的極化目標(biāo)分解來(lái)獲取建筑物的極化目標(biāo)分解參數(shù),如Cloude分解、Freeman分解、Yamaguchi分解等。圓極化相關(guān)系數(shù)對(duì)建筑物比較敏感,結(jié)合圓極化相關(guān)系數(shù)和極化目標(biāo)分解參數(shù),通過(guò)Wishart分類器將極化SAR影像分為建筑物和非建筑物,從而區(qū)別出極化SAR影像中的建筑物和非建筑物,實(shí)現(xiàn)建筑物的檢測(cè),實(shí)驗(yàn)結(jié)果表明結(jié)合極化目標(biāo)分解參數(shù)和圓極化相關(guān)系數(shù)能夠有效地從極化SAR影像中檢測(cè)出建筑物。(3)綜合極化SAR影像中建筑物的紋理特征、極化散射特征來(lái)檢測(cè)建筑物。利用加權(quán)特征融合的方法將建筑物的紋理特征和極化散射特征等多種特征有效地結(jié)合在一起構(gòu)成建筑物的特征集,再通過(guò)SVM分類器將極化SAR影像中的地物目標(biāo)分為建筑物和非建筑物,從而區(qū)別出極化SAR影像中的建筑物和非建筑物,且得到建筑物的整體檢測(cè)結(jié)果較好,還對(duì)各檢測(cè)結(jié)果進(jìn)行分析和評(píng)價(jià),實(shí)驗(yàn)結(jié)果表明基于多特征融合的建筑物檢測(cè)結(jié)果在檢出率和正確率等評(píng)價(jià)指標(biāo)上都有所提高,因此綜合紋理特征和極化散射特征對(duì)建筑物檢測(cè)有較好的影響。
[Abstract]:The detection and recognition of ground objects using polarimetric synthetic aperture radar (SAR) images is an important research topic in the interpretation of polarimetric SAR. The land use survey of buildings has been detected from the polarimetric SAR images. The application of urban planning and urban change monitoring has important theoretical significance and practical value. However, because of the complex geometric structure distribution of buildings, And the cross-polarization scattering of buildings often leads them to be misclassified into other ground objects such as forests. Therefore, it is still a challenging task to detect buildings from polarized SAR images. In this paper, the geometric features, texture features and polarimetric scattering features of high resolution polarimetric SAR images are used to detect buildings. The texture features of buildings are combined with their polarimetric scattering features to detect buildings from polarized SAR images. In this paper, the L-band space-borne ALOS-2 PALSAR-2 fully polarized SAR images and airborne E-SAR all-polarized SAR images are used as experimental data. The geometric features, texture features and polarimetric scattering features of buildings in polarized SAR images are used to detect buildings, respectively. And the texture feature and polarization scattering feature are combined by weighted feature fusion method to realize building detection. The main research work and conclusions are as follows: 1) using the method of tagged watershed transform based on high resolution polarimetric SAR image to detect the building. The contour information of the building is presented in the polarized SAR image. The main geometric features that appear, The building is detected from high resolution polarized SAR images by using the marked watershed transform method based on the geometric features of buildings. The experimental results show that the proposed method has a good preserving degree for building edge profile information. (2) based on the polarimetric scattering characteristics, the building detection of fully polarized SAR images is carried out. In this paper, the polarimetric scattering characteristics of buildings are presented. The characteristics mainly include polarization target decomposition parameters and circular polarization correlation coefficients. Several commonly used polarization target decomposition parameters are mainly used to obtain the polarization target decomposition parameters of buildings. For example, Cloude decomposition Freeman decomposition and Yamaguchi decomposition. Circular polarization correlation coefficient is sensitive to buildings. Combined with circular polarization correlation coefficient and polarimetric target decomposition parameters, polarimetric SAR images are divided into buildings and non-buildings by Wishart classifier. In order to distinguish the building from the non-building in the polarized SAR image, the detection of the building can be realized. The experimental results show that the texture features of buildings in polarimetric SAR images can be effectively detected from polarized SAR images by combining polarization target decomposition parameters with circular polarization correlation coefficients. Using the weighted feature fusion method, the texture feature and the polarization scattering feature of the building are effectively combined to form the building feature set. Then the ground objects in polarized SAR images are divided into buildings and non-buildings by SVM classifier, and the buildings in polarized SAR images are distinguished from non-buildings, and the overall detection results of buildings are obtained. The experimental results show that the detection results of buildings based on multi-feature fusion have been improved in terms of detection rate and accuracy. Therefore, the integrated texture feature and polarization scattering feature have a good effect on building detection.
【學(xué)位授予單位】：電子科技大學(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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