共查询到20条相似文献，搜索用时 78 毫秒
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面向数字城市和智慧城市建设急需城市建筑三维模型支撑的需要,本文基于机载LiDAR数据,以“顾及平整性的屋顶面片分割—屋顶层间连接—三维模型重建”为脉络,提出了一种采用层间连接和平滑策略的建筑屋顶三维模型重建方法。在屋顶面片提取过程中,充分顾及了屋顶面片的平整性;并在屋顶面片平整基础上,提出层间连接点的概念,以实现高效、快速的模型重建工作。试验部分,本文从屋顶面片重建完整率与正确率、重建几何精度及建筑物高程对于重建的影响3个方面作了较为详尽的评价与分析,并在国际摄影测量与遥感学会标准数据集支撑下,与国际同行进行试验对比。试验结果表明,建筑屋顶重建的完整率和正确率分别达到90%和95%;在偏移距离评价方面,平均偏移距离和标准差最优分别达0.05 m和0.18 m。因此,本文方法可有效完成建筑屋顶三维模型重建,重建模型准确度高、完整性好。 相似文献
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Maher Ibrahim Sameen Biswajeet Pradhan 《Journal of the Indian Society of Remote Sensing》2017,45(3):395-405
Information on highways is an essential input for various geospatial applications, including car navigation, forensic analysis on highway geometries, and intelligent transportation systems. Semi-automatic and automatic extractions of highways are critical for the regular updating of municipal databases and for highway maintenance. This study presents a semi-automatic data processing approach for extracting highways from high-resolution airborne LiDAR height information and aerial orthophotos. The method was developed based on two data sets. Experimental results for the first testing site showed that the accuracy of the proposed method for highway extraction was 74.50 % for completeness and 73.13 % for correctness. Meanwhile, the completeness and correctness for the second testing site were 71.20 and 70.72 %, respectively. The proposed method was compared with an object-based approach on a different data set. The accuracy for highway extraction of the object-based approach was 64.29 % for completeness and 63.11 % for correctness, whereas that of the proposed method was 67.14 % for completeness and 65.08 % for correctness. This research aims to promote semi-automatic highway extraction from LiDAR data and orthophotos by proposing a new approach and a multistep post-processing technique. The proposed method provides an accurate final output that is valuable for a wide range of geospatial applications. 相似文献
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Zeynep Akbulut Samed Özdemir Hayrettin Acar Fevzi Karsli 《Journal of the Indian Society of Remote Sensing》2018,46(12):2057-2068
Automatic building extraction is an important topic for many applications such as urban planning, disaster management, 3D building modeling and updating GIS databases. Its approaches mainly depend on two data sources: light detection and ranging (LiDAR) point cloud and aerial imagery both of which have advantages and disadvantages of their own. In this study, in order to benefit from the advantages of each data sources, LiDAR and image data combined together. And then, the building boundaries were extracted with the automated active contour algorithm implemented in MATLAB. Active contour algorithm uses initial contour positions to segment an object in the image. Initial contour positions were detected without user interaction by a series of image enhancements, band ratio and morphological operations. Four test areas with varying building and background levels of detail were selected from ISPRS’s benchmark Vaihingen and Istanbul datasets. Vegetation and shadows were removed from all the datasets by band ratio to improve segmentation quality. Subsequently, LiDAR point cloud data was converted to raster format and added to the aerial imagery as an extra band. Resulting merged image and initial contour positions were given to the active contour algorithm to extract building boundaries. In order to compare the contribution of LiDAR to the proposed method, the boundaries of the buildings were extracted from the input image before and after adding LiDAR data to the image as a layer. Finally extracted building boundaries were smoothed by the Awrangjeb (Int J Remote Sen 37(3): 551–579. https://doi.org/10.1080/01431161.2015.1131868, 2016) boundary regularization algorithm. Correctness (Corr), completeness (Comp) and accuracy (Q) metrics were used to assess accuracy of segmented building boundaries by comparing extracted building boundaries with manually digitized building boundaries. Proposed approach shows the promising results with over 93% correctness, 92% completeness and 89% quality. 相似文献
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以机载LiDAR点云数据为研究对象,提出一种新的基于点云数据的多层建筑物三维轮廓模型高精度自动重建方法。在已完成建筑物结构提取及轮廓规则化处理的基础上,利用多层屋顶轮廓在水平投影面内的相邻关系,将各层屋顶中同等级屋顶的相邻关系概括为平行边、不平行且不相交、相交3种相邻形式,结合多层屋顶的层级结构信息对相邻轮廓边界进行一致性处理。实验证明本文方法可以进一步消除多层建筑物各屋顶轮廓的规则化处理误差,使相邻轮廓边界在水平投影面内严格重合,同时重建后建筑物三维轮廓模型的正确性与完整性较高,拐点的定位精度优于激光点平均间距。 相似文献
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The existing roadway infrastructures are mostly archived with two-dimensional (2D) drawings that lack the possibility for three-dimensional (3D) interpretation and advanced 3D analysis. The mobile LiDAR system (MLS) is gaining popularity in 3D mapping applications along various types of road corridors. MLS achieves the highest data quality and completeness among the traditional roadway data collection methods. The rural roads in different countries especially in India form a substantial portion of the road network. Therefore the proper maintenance and road safety analysis of rural roads are recommended activity, which could be addressed using detailed 3D road surface information. The absence of raised curb at road boundary, and presence of complexity, heterogeneity and occlusions along the rural roadway settings restrict the use of existing studies for road surface extraction using MLS point cloud data. Therefore considering the above requirement, this research paper proposes a two-stage method. The first stage extract planar ground surfaces which are further used to filter road surface in the second stage. Global properties of road, that is, topology and smoothness and its radiometric response to laser beam of MLS are used in the second stage. MLS point cloud data of rural roadway were used to test the proposed method. The road surface points were accurately extracted without being affected by the absence of raised curb and hanging objects over the road surface, that is, tree canopies and overhead power lines. The quantitative assessment of the proposed method was performed in terms of correctness, completeness and quality, which were 96.3, 94.2, and 90.9%, respectively. 相似文献
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Wei Yao Stefan Hinz Uwe Stilla 《ISPRS Journal of Photogrammetry and Remote Sensing》2011,66(3):260-271
Airborne LiDAR data are characterized by involving not only rich spatial but also temporal information. It is possible to extract vehicles with motion artifacts from single-pass airborne LiDAR data, based on which the motion state and velocity of vehicles can be identified and derived. In this paper, a complete strategy for urban traffic analysis using airborne LiDAR data is presented. An adaptive 3D segmentation method is presented to facilitate the task of vehicle extraction. The method features an ability to detect local arbitrary modes at multi scales, thereby making it particularly appropriate for partitioning complex point cloud data. Vehicle objects are then extracted by a binary classification using object-based features. Furthermore, the motion analysis for extracted vehicles is performed to distinguish between moving and stationary ones. Finally, the velocity is estimated for moving vehicles. The applicability and efficiency of the presented strategy is demonstrated and evaluated on three ALS datasets acquired for the propose of city mapping, where up to 87% of vehicles have been extracted and up to 83% of moving traffic can be recovered together with reasonable velocity estimates. It can be concluded that airborne LiDAR data can provide value-added products for traffic monitoring applications, including vehicle counts, location and velocity, along with traditional products such as building models, DEMs and vegetation models. 相似文献
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一种改进顶帽变换与LBP高程纹理的城区建筑物提取算法 总被引：1，自引：1，他引：0
利用LiDAR数据的建筑物提取存在植被点与建筑物点难以区分的问题,利用航空影像进行城区建筑物提取则无法有效剔除阴影区域植被。本文融合LiDAR和航空影像两种数据源,提出了改进顶帽变换及局部二进制模式(LBP)高程纹理分析的建筑物提取算法。首先将LiDAR数据进行规则格网化,通过改进顶帽变换提取地面数据点,然后根据航空影像计算归一化差值植被指数(NDVI)值进行植被粗提取,计算LBP高程纹理,精细区分植被点与建筑物点,最后利用形态学操作填充建筑物孔洞,以检测出的建筑物点为种子点进行区域生长,得到完整的建筑物点集合。试验基于ISPRS提供的Vaihingen数据集中复杂多植被城区场景,试验结果表明,本文算法能够有效区分植被与建筑物,实现建筑物准确提取。 相似文献
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面向对象的形态学建筑物指数及其高分辨率遥感影像建筑物提取应用 总被引：3，自引：3，他引：0
高分辨率遥感影像建筑物提取是摄影测量与遥感领域的一个热门研究主题。本文综合利用影像分割、基于图的数学形态学top-hat重建技术,提出了面向对象的形态学建筑物指数OBMBI,并将其应用于高分辨率遥感影像建筑物提取。首先,建立像素-对象-图节点的双向映射关系;然后,基于图的白top-hat重建和上述映射关系来构建OBMBI图像;接着,对该OBMBI图像二值化、矢量化以获取建筑物多边形;最后,对结果进行后处理优化。使用一景航空、一景卫星全色影像对本文方法和PanTex方法进行性能测试。试验表明,本文方法的建筑物提取精度显著的优于PanTex方法。其中,本文方法平均比PanTex方法的正确率高9.49%、完整率高11.26%、质量高14.11%。 相似文献
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This article suggests a new approach to automatic building footprint modeling using exclusively airborne LiDAR data. The first part of the suggested approach is the filtering of the building point cloud using the bias of the Z‐coordinate histogram. This operation aims to detect the points of roof class from the building point cloud. Hence, eight rules for histogram interpretation are suggested. The second part of the suggested approach is the roof modeling algorithm. It starts by detecting the roof planes and calculating their adjacency matrix. Hence, the roof plane boundaries are classified into four categories: (1) outer boundary; (2) inner plane boundaries; (3) roof detail boundaries; and (4) boundaries related to the missing planes. Finally, the junction relationships of roof plane boundaries are analyzed for detecting the roof vertices. With regard to the resulting accuracy quantification, the average values of the correctness and the completeness indices are employed in both approaches. In the filtering algorithm, their values are respectively equal to 97.5 and 98.6%, whereas they are equal to 94.0 and 94.0% in the modeling approach. These results reflect the high efficacy of the suggested approach. 相似文献
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建筑物轮廓作为建筑物三维重建的重要元素,在建立智慧城市和数字城市中至关重要。本文针对从机载激光雷达点云中提取建筑物轮廓数据处理的点云滤波、建筑物屋顶面提取、建筑物轮廓提取,以及提取精度评定各环节存在的一些问题,提出了一种综合区域生长改进算法、三维Hough变换算法和α-shape算法的建筑物轮廓提取方法。该方法在对机载LiDAR点云数据去噪的基础上,首先利用改进的区域生长算法滤波地面点,并基于地物点到地面的归一化高程特征通过高度阈值去除高度较为低矮的地物点;再基于三维Hough变换算法从剩余建筑物和高大树木点云中提取建筑物平面;最后使用α-shape算法提取建筑物的轮廓信息。对使用RIEGLVQ-1560i机载激光雷达测量系统扫描的某城区点云数据进行计算,通过匹配度、形状相似度和位置精度等评价指标对提取的建筑物轮廓进行精度评定。结果表明,综合区域生长改进算法、三维Hough变换算法和α-shape算法的建筑物轮廓提取方法可以准确提取建筑物的轮廓信息,对于大范围的建筑物轮廓提取具有稳定性和普遍适用性。 相似文献
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Object-based image analysis (OBIA) has been a new area of research in satellite image processing applications, since it improves the quality of information acquisition about geospatial objects and also enables to add spatial and contextual information to the objects of interest. The extraction of buildings from High Resolution Satellite (HRS) image in an urban scenario has been an intricate problem due to their different size, shape, varying rooftop textures and low contrast between building and surrounding region. In this study, a new object-based automatic building extraction technique has been proposed to extract building footprints from HRS pan sharpened IKONOS multispectral image. The study is mainly emphasizing on obtaining optimal values for segmentation parameters, shape parameters, and defining rule set to extract buildings and eliminate misclassified other urban features. The suitability of the technique has been judged using different indicators, such as, completeness, correctness and quality. 相似文献
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建筑物提取一直是机载激光点云数据处理研究的热点,其中建筑物和其他地物之间的区分是研究的核心和难点。为提高建筑物与其他地物在机载激光点云中的区分能力,提出了一种建筑物点云层次提取方法。首先,在点云滤波后,从非地面点云中提取建筑物候选区域;然后,通过形态学重建和点云平面分割方法对建筑物候选区域构建多尺度空间,并建立目标区域的拓扑关系图;最后,在拓扑关系图基础上,利用5种特征量对目标区域分类,并精确提取建筑物点云。为了测试算法的有效性和可靠性,利用国际摄影测量与遥感学会（International Society for Photogrammetry and Remote Sensing,ISPRS）提供的Vaihingen和Toronto两组测试数据集进行实验,并由ISPRS对结果进行评估,其中基于面积和目标的完整度、正确率和提取质量分别都大于87.8%、94.7%、87.3%。与其他建筑物提取方法相比,该方法在基于面积和目标的质量指标方面最为稳定。实验结果表明,在不同的城市场景下,该算法能够稳健地提取建筑物,并保持很高的正确率。 相似文献
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There are now a wide range of techniques that can be combined for image analysis. These include the use of object-based classifications rather than pixel-based classifiers, the use of LiDAR to determine vegetation height and vertical structure, as well terrain variables such as topographic wetness index and slope that can be calculated using GIS. This research investigates the benefits of combining these techniques to identify individual tree species. A QuickBird image and low point density LiDAR data for a coastal region in New Zealand was used to examine the possibility of mapping Pohutukawa trees which are regarded as an iconic tree in New Zealand. The study area included a mix of buildings and vegetation types. After image and LiDAR preparation, single tree objects were identified using a range of techniques including: a threshold of above ground height to eliminate ground based objects; Normalised Difference Vegetation Index and elevation difference between the first and last return of LiDAR data to distinguish vegetation from buildings; geometric information to separate clusters of trees from single trees, and treetop identification and region growing techniques to separate tree clusters into single tree crowns. Important feature variables were identified using Random Forest, and the Support Vector Machine provided the classification. The combined techniques using LiDAR and spectral data produced an overall accuracy of 85.4% (Kappa 80.6%). Classification using just the spectral data produced an overall accuracy of 75.8% (Kappa 67.8%). The research findings demonstrate how the combining of LiDAR and spectral data improves classification for Pohutukawa trees. 相似文献
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Fuel type mapping of the wildland-urban interface (WUI) in support of fire spread simulation modelling should include both natural and urban features. The objective of this study was to evaluate the utility of (1) Light Detection and Ranging (LiDAR) structural data, (2) ortho-image data and (3) a combination of both as input to an object-based classification approach for mapping fuels within two WUI areas in San Diego, California. A separability analysis was utilized to determine the surface topographical and spectral layers most influential for discriminating WUI fuels. An accuracy assessment revealed that the combination of LiDAR and ortho-image data inputs substantially increased classification accuracy by 20–30% and achieved overall accuracies?>?80%. Results from the study provide knowledge on how reliable fuel types within the WUI can be mapped using high-resolution LiDAR and ortho-image data while presenting new insights into fuel type mapping. 相似文献
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A Hough transform based approach for extraction of buildings using LiDAR data is presented. It is argued that LiDAR data should be smoothed and sparsed prior to Hough transform for better result. Algorithms to realize this are presented. Further, an algorithm which fits a vector model to extracted buildings is outlined. Simulated LiDAR data have been used to investigate the effect of three parameters (data density, flying height, and scan angle) on the quality of buildings extracted. A set of accuracy indices is proposed for this purpose. It is shown that the data density is the most significant parameter affecting the accuracy of building identification. 相似文献
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激光雷达技术(LiDAR)已广泛应用于数字高程模型(DEM)的快速获取和三维城市模型的建立中,但仍有许多不足之处,需要做更深入的研究。本文介绍了一种新的建筑物提取方法,称之为Fc-S法。该方法首先利用等高线特征进行滤波,从LIDAR数据内插的数字表面模型(DSM)中提取出DEM,利用DSM与DEM的高差阈值和DSM边缘特征参数去掉地面点和汽车等矮小物体,获得主要包含植被和建筑物的地物点群,然后对地物点群进行分割,利用二次梯度和面积等参数去掉植被点,并采用迭代逼近的方法精化建筑物。文章通过实验对所提方法进行验证,并借助高分辨率的航空影像对建筑物提取结果进行评估,评估结果表明该方法能够在地形起伏的区域中较准确地提取出建筑物。 相似文献
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城区机载LiDAR数据与航空影像的自动配准 总被引：2，自引：0，他引：2
为解决机载LiDAR数据与航空影像集成应用中二者的配准问题,提出了一种机载LiDAR数据与航空影像配准的方法。首先,直接在LiDAR点云中提取建筑物3维轮廓线,通过将轮廓线规则化得到由两条相互垂直的直线段组成的建筑物角特征,并在航空影像上提取直线特征;然后,根据影像初始外方位元素将建筑物角特征投影到航空影像上,并采用一定的相似性测度在影像上寻找同名的影像角特征;最后,将角特征的角点当作控制点,利用传统的摄影测量光束法区域网平差解求影像新的外方位元素。解算过程中采用循环迭代策略。本方法的主要特点是,直接从LiDAR点云中提取线特征,避免了常规方法从距离图(或强度图)中提取线特征所产生的内插误差。通过与现有基于点云强度图的配准方法的对比实验表明,在低精度初始外方位元素的辅助下,本文方法能够达到较高的配准精度。 相似文献
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半自动机载LiDAR点云建筑物三维重建方法 总被引：1，自引：1，他引：0
针对全自动建筑物3D重建存在需要后续人工检验,且发现重建错误需要花费额外时间修改的问题,提出了一种半自动的面向对象的机载LiDAR点云建筑物3D重建方法。基于建筑物类别点云的联通分析和平面生长分割结果,提出了自动的建筑物栋数检测、单栋建筑物外轮廓提取、单栋建筑物内部结构线提取方法;同时,在计算机无法完成部分工作时,人工辅助计算机完成高程阶越线提取、识别建筑物屋顶附属物点云等工作。实验证明,该方法可以适用于高密度机载LiDAR点云数据中城区大部分建筑物的3D模型重建。 相似文献