本文選題：LiDAR　切入點(diǎn)：高分影像　出處：《西南交通大學(xué)》2013年碩士論文　論文類型：學(xué)位論文

【摘要】：針對地震災(zāi)區(qū)地形復(fù)雜,地物光譜特征、紋理特征與空間分布復(fù)雜,利用單一數(shù)據(jù)源無法快速、準(zhǔn)確地提取損毀建筑物信息等情況,本文利用高分影像所具有的光譜特征、紋理特征和LiDAR點(diǎn)云所具有的高精度的空間三維信息、二次回波信息應(yīng)用面向?qū)ο蟮挠跋穹诸惣夹g(shù),以實(shí)現(xiàn)損毀建筑物的快速、準(zhǔn)確提取為目的,開展了以下研究工作： 1、歸納總結(jié)了現(xiàn)有LiDAR點(diǎn)云濾波分類方法、LiDAR點(diǎn)云與高分影像的配準(zhǔn)方法。采取人機(jī)交互的方式,利用基于面元特征的配準(zhǔn)方法實(shí)現(xiàn)了LiDAR點(diǎn)云和高分影像的配準(zhǔn)。 2、綜述了面向?qū)ο蟮母叻钟跋穹指罘诸惙椒?主要包括：高分影像的多尺度分割方法、最優(yōu)分割尺度確定以及面向?qū)ο蟮哪：诸惙椒ā?3、研究采用LiDAR點(diǎn)云以nDSM的方式參與多尺度分割,實(shí)驗(yàn)表明nDSM作為LiDAR點(diǎn)云的“高程”信息參與多尺度分割并不會造成影像的“過分割”現(xiàn)象,而是加速了影像對象的合并過程。 4、本文提出最優(yōu)分割尺度范圍值求交、取最小值得到最優(yōu)分割尺度的方法,該方法優(yōu)于原有的最優(yōu)分割尺度確定方法。實(shí)驗(yàn)表明該方法是可行的,該方法具有更廣的適用范圍。 5、研究分析了實(shí)驗(yàn)研究區(qū)域內(nèi)各地物目標(biāo)的光譜特征、形狀特征、紋理特征等,通過樣本的選擇、實(shí)驗(yàn)分析選定了各地物目標(biāo)的分類特征,建立了模糊分類規(guī)則集。根據(jù)樹木在高分影像上的光譜特征構(gòu)造了新的植被指數(shù)(VInew),實(shí)驗(yàn)表明：利用該指數(shù)能夠準(zhǔn)確有效的提取出樹木信息,提取樹木信息的Kappa系數(shù)為0.981744。 6、根據(jù)損毀建筑物的特征,采用通過逐步剔除影像上的樹木、道路、建筑物的方式提取損毀建筑物。最后對面向?qū)ο蟮膿p毀建筑物提取結(jié)果進(jìn)行了精度分析,實(shí)驗(yàn)表明該方法切實(shí)可行,提取損毀建筑物Kappa系數(shù)為0.932576。 綜上,本文研究的基于LiDAR點(diǎn)云與高分影像的面向?qū)ο蟮膿p毀建筑物提取方法是切實(shí)可行的,損毀建筑物提取結(jié)果的用戶精度為94.04%,生產(chǎn)者精度為95.30%,Kappa系數(shù)為0.932576。本文的研究成果對于實(shí)現(xiàn)損毀建筑物的快速、準(zhǔn)確提取具有一定的參考價(jià)值,為災(zāi)后建筑物損毀評估、應(yīng)急響應(yīng)的信息獲取提供了可靠的技術(shù)支持。
[Abstract]:In view of the complex terrain, spectral features, texture features and spatial distribution of the quake-hit areas, the information of damaged buildings can not be extracted quickly and accurately by using a single data source, and the spectral features of high-score images are used in this paper. Texture feature and LiDAR point cloud have high precision spatial 3D information. The secondary echo information applies object oriented image classification technology, in order to realize the fast and accurate extraction of damaged buildings, the following research work is carried out:. 1. The existing LiDAR point cloud filtering classification methods are summarized, and the registration method of LiDAR point cloud and high score image is realized by using the method of human-computer interaction. 2. The object-oriented high-score image segmentation and classification methods are summarized, including multi-scale segmentation method, optimal segmentation scale determination and object-oriented fuzzy classification method. 3. Using LiDAR point cloud to participate in multi-scale segmentation by means of nDSM, the experiment shows that nDSM, as the "elevation" information of LiDAR point cloud, does not result in "over-segmentation" of image, but accelerates the merging process of image objects. 4. In this paper, the method of finding the intersection of the range value of the optimal segmentation scale and obtaining the optimal partition scale by taking the minimum value is proposed. The method is superior to the original method for determining the optimal segmentation scale. The experiment shows that the method is feasible and has a wider range of application. 5. The spectral feature, shape feature, texture feature and so on of the objects in the experimental study area are analyzed. Through the selection of samples, the classification features of the objects are selected. A fuzzy classification rule set was established, and a new vegetation index was constructed according to the spectral characteristics of trees in high score images. The experimental results show that the Kappa coefficient of tree information can be extracted accurately and effectively by using this index, and the Kappa coefficient of extracting tree information is 0.981744. 6. According to the characteristics of damaged buildings, the damaged buildings are extracted by gradually removing the trees, roads and buildings on the image. Finally, the precision analysis of the result of the object oriented damaged building extraction is carried out. The experimental results show that this method is feasible and the Kappa coefficient of damaged buildings is 0.932576. In summary, the method of object oriented damage building extraction based on LiDAR point cloud and high score image is feasible. The user accuracy of the damaged building extraction results is 94.04 and the producer accuracy is 95.30 and the Kappa coefficient is 0.932576.The research results in this paper have certain reference value for the rapid and accurate extraction of damaged buildings, and are useful for the assessment of building damage after the disaster. The information acquisition of emergency response provides reliable technical support.
【學(xué)位授予單位】：西南交通大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2013
【分類號】：P225.2

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本文編號：1576139