發(fā)布時間：2018-03-22 13:38

  本文選題：機載LiDAR　切入點：數(shù)據(jù)濾波　出處：《南京大學(xué)》2013年碩士論文　論文類型：學(xué)位論文

【摘要】：建筑物的識別與提取一直是測繪、遙感領(lǐng)域研究的熱點。建筑物作為城區(qū)地面的重要特征在圖像特征匹配、地形圖更新、數(shù)字城市等領(lǐng)域起重要作用。機載LiDAR作為一種新興的對地觀測技術(shù),能夠快速實現(xiàn)地表三維信息的識別與提取。如何從海量的LiDAR點云數(shù)據(jù)中快速自動化地實現(xiàn)建筑物提取,具有十分重要的意義。 本文在總結(jié)前人研究的基礎(chǔ)上,提出了一種基于TIN模型的點云分割濾波算法以及一套基于輪廓線的建筑物邊界多邊形矢量化算法。主要研究內(nèi)容和結(jié)論如下： (1)典型地物L(fēng)iDAR點云空間分布規(guī)律：點云分布特征不僅與相應(yīng)地物表面物質(zhì)特性有關(guān),還與其表面平坦與否相關(guān)。本文深入地分析了典型地物相應(yīng)的LiDAR點云的空間分布規(guī)律,得出噪聲點多呈孤立點分布、植被點云團簇狀分布而方差較大、地面平坦而高程差較小、水體因吸收作用,數(shù)據(jù)點稀疏、地面突出物體積小,腳點數(shù)量少、建筑物點云分布規(guī)律,點密度均勻等結(jié)論。 (2)機載LiDAR濾波算法：在綜合評價幾類經(jīng)典濾波算法的基礎(chǔ)上,提出了一種基于TIN模型的點云分割算法。本算法通過構(gòu)建TIN模型建立點云之間的空間鄰接關(guān)系,采用聚類的思想對點云進行分割,使點云聚類成內(nèi)部均勻的對象,然后根據(jù)對象的高程與方差過濾出建筑物點,完成濾波。選擇了三個典型的研究區(qū)(10。左右的斜坡、含粗差的不規(guī)則復(fù)雜建筑物、居民區(qū))進行實驗,實驗結(jié)果表明：斜坡的濾波總體精度達96.61%,Kappa系數(shù)達0.9154；復(fù)雜建筑物的濾波總體精度達91.69%,Kappa系數(shù)達0.8183；大片居民區(qū)的濾波精度達93.72%,Kappa系數(shù)達0.8076.說明本算法具有很好的抗粗差能力,對斜坡、復(fù)雜建筑物等也具有較好的濾波效果,適合城區(qū)建筑物L(fēng)iDAR數(shù)據(jù)濾波。 (3)建筑物輪廓多邊形矢量化：本文應(yīng)用Alpha-Shapes算法追蹤建筑物點集的輪廓線,實現(xiàn)了凹型建筑物及構(gòu)造復(fù)雜建筑物的內(nèi)外輪廓線的有效提�。黄浯�,本文提出的改進的最小方向差模型擬合算法保證了所計算出的建筑物主方向與初始輪廓多邊形所有線段之間的方向差達到最小。實驗表明主方向估計值幾乎無偏差的概率達69.4%,微小偏差的概率為26.5,大偏差的概率僅為4.1%,提取精度遠遠高于常用的基于Hough變換、主成分變換、統(tǒng)計直方圖等方法。另外,建筑物輪廓多邊形規(guī)范化方法對初步規(guī)范化后的多邊形進行邊界擴展,使擴展后的多邊形線段能夠定位于建筑物邊緣點。
[Abstract]:The identification and extraction of buildings has been a hot topic in the field of surveying and remote sensing. As an important feature of urban ground, buildings are matched in image features and updated in topographic maps. As a new technology of Earth observation, airborne LiDAR can quickly realize the recognition and extraction of 3D surface information. How to quickly and automatically realize building extraction from massive LiDAR point cloud data. It is of great significance. On the basis of summarizing the previous studies, this paper proposes a point cloud segmentation filtering algorithm based on TIN model and a set of building boundary polygon vectorization algorithm based on contour. The main contents and conclusions are as follows:. 1) the spatial distribution law of LiDAR point cloud of typical ground objects: the distribution characteristics of point clouds are not only related to the surface properties of the corresponding ground objects, but also related to the flatness of the surface. The spatial distribution law of the corresponding LiDAR point clouds of typical ground objects is analyzed in depth in this paper. It is concluded that the noise points are mostly distributed in isolated points, the vegetation point clouds are clustered and the variance is large, the ground is flat and the elevation difference is small, the water body is absorbed, the data points are sparse, the surface protrusions are small in volume and the number of foot points is small. The rule of point cloud distribution and the uniform point density of the building are obtained. (2) Airborne LiDAR filtering algorithm: on the basis of synthetically evaluating several classical filtering algorithms, a point cloud segmentation algorithm based on TIN model is proposed. The spatial adjacency relationship between point clouds is established by constructing TIN model. The idea of clustering is used to segment the point cloud, so that the point cloud is clustered into an internal uniform object. Then, according to the height and variance of the object, the building points are filtered out to complete the filtering. Irregular and complex buildings, residential areas, with gross errors. The experimental results show that the overall filtering accuracy of slope is 96.61 and that of Kappa coefficient is 0.9154, that of complex building is 91.69 and that of Kappa is 0.8183, that of large residential area is 93.72and Kappa is 0.8076.The result shows that the algorithm has a good ability to resist gross error, and has good ability to resist gross error. Complex buildings also have better filtering effect, which is suitable for LiDAR data filtering of urban buildings. (3) Vectorization of building contour polygon: in this paper, Alpha-Shapes algorithm is used to trace the contour line of the building point set, which can effectively extract the inner and outer contour lines of concave building and complex building. Secondly, The improved least direction difference model fitting algorithm proposed in this paper ensures the minimum direction difference between the main direction of the building and all the lines of the initial contour polygon. The experimental results show that the estimated value of the principal direction is almost unbiased. The probability of difference is 69.4, the probability of small deviation is 26.5, the probability of large deviation is only 4.1, and the precision of extraction is much higher than that of Hough transform. In addition the method of building contour polygon normalization extends the boundary of the primary normalized polygon so that the expanded polygon line segment can locate at the edge of the building.
【學(xué)位授予單位】：南京大學(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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