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Accurate detection and extraction of individual trees is one of hottest topics, which can be widely used in vehicles navigation, tree modeling, tree growth monitoring and urban green quantity estimation. The difficulty associated with individual trees extraction is the occlusion with other objects in cluttered point clouds of urban scenes, which inhibits the automatic extraction of individual trees. In this paper, we present a comprehensive framework that can be used to extract individual trees from terrestrial scanned outdoor scene. In our framework, a bottom-up method by shape-guided classification is achieved to select the candidate tree crowns and tree trunks, and a novel three-stage shape merging rule containing localization, filtering, and matching (LFM) are proposed to generate a complete individual tree. The primary advantage of the proposed method is that it is independent of the quality of data and different shapes. We made comparison experiments of classification methods of support vector machine and random forest on the accuracy assessment. The effectiveness of the proposed framework was tested in five street scenarios in point clouds from Oakland outdoor MLS dataset. The results for the five test sites achieved tree detection rates higher than 97%; the overall accuracy was approximately 98%, and the completion quality of both procedures was 96%. Non-detected trees are always sparse which come from occlusions in the point cloud data; most misclassifications occurred in man-made pillars adjacent to trees and have the same height with tree trunk. Comparison experiments to the existing methods are made to illustrate the effectiveness of our method.
Xiaojuan Ning; Ge Tian; Yinghui Wang. Shape classification guided method for automated extraction of urban trees from terrestrial laser scanning point clouds. Multimedia Tools and Applications 2021, 1 -19.
AMA StyleXiaojuan Ning, Ge Tian, Yinghui Wang. Shape classification guided method for automated extraction of urban trees from terrestrial laser scanning point clouds. Multimedia Tools and Applications. 2021; ():1-19.
Chicago/Turabian StyleXiaojuan Ning; Ge Tian; Yinghui Wang. 2021. "Shape classification guided method for automated extraction of urban trees from terrestrial laser scanning point clouds." Multimedia Tools and Applications , no. : 1-19.
Indoor scene reconstruction from point cloud data provided by Terrestrial laser scanning (TLS) has become an issue of major interest in recent years. However, the raw scanned indoor scene is always complex with severe noise, outliers and incomplete regions, which produces more difficulties for indoor scene modeling. In this paper, we presented an automatic approach to reconstruct the structure of indoor scene from point clouds acquired by registering several scans. Our method first extracts different candidate walls by separating the indoor scene into different planes based on normal variation. Then the boundary of those candidate walls are obtained by projecting them onto 2D planes. We classify the walls into exterior wall and interior wall by clustering. After distinguishing the 3D points belonging to exterior walls, a simple strategy is generated to refine the 3D model of wall structure. The methodology has been tested on three real datasets, which constitute of different varieties of indoor scenes. The results derived reveal that the indoor scene could be correctly extracted and modeled.
Xiaojuan Ning; Jie Ma; Zhiyong Lv; Qingzheng Xu; Yinghui Wang. Structure Reconstruction of Indoor Scene from Terrestrial Laser Scanner. Model Checking Software 2019, 91 -98.
AMA StyleXiaojuan Ning, Jie Ma, Zhiyong Lv, Qingzheng Xu, Yinghui Wang. Structure Reconstruction of Indoor Scene from Terrestrial Laser Scanner. Model Checking Software. 2019; ():91-98.
Chicago/Turabian StyleXiaojuan Ning; Jie Ma; Zhiyong Lv; Qingzheng Xu; Yinghui Wang. 2019. "Structure Reconstruction of Indoor Scene from Terrestrial Laser Scanner." Model Checking Software , no. : 91-98.
During the data acquisition procedure, volume data are usually contaminated by noises. This would create visual confusion and misunderstanding in analyzing the volume data. Thus noise reduction is necessary for improving the quality of volume data inspection and analytic tasks. However, it is far from being fully resolved for removing noise while maximally retaining geometric sharp features. In the work, we present a powerful volume denoising method based on the extended weighted least squares. We improve the weighted least squares method and extend it to 3D for volume data denoising. The primary advantage of the proposed method is that it can consistently produce better results for removing noise while preserving sharp features. We illustrate our technique on synthetic and real-world 3D data and compare our method with median method, weighted least squares, L0 volume gradient minimization and edge aware anisotropic diffusion method, the experimental results demonstrate that our method can achieve higher quality results than the selected state-of-the art methods.
Huanhuan Zhang; Yinghui Wang; Xiaojuan Ning; Ke Lv; Ningna Wang. Volume Data Denoising via Extended Weighted Least Squares. IEEE Access 2018, 7, 2750 -2758.
AMA StyleHuanhuan Zhang, Yinghui Wang, Xiaojuan Ning, Ke Lv, Ningna Wang. Volume Data Denoising via Extended Weighted Least Squares. IEEE Access. 2018; 7 (99):2750-2758.
Chicago/Turabian StyleHuanhuan Zhang; Yinghui Wang; Xiaojuan Ning; Ke Lv; Ningna Wang. 2018. "Volume Data Denoising via Extended Weighted Least Squares." IEEE Access 7, no. 99: 2750-2758.
Outlier removal is a fundamental data processing task to ensure the quality of scanned point cloud data (PCD), which is becoming increasing important in industrial applications and reverse engineering. Acquired scanned PCD is usually noisy, sparse and temporarily incoherent. Thus the processing of scanned data is typically an ill-posed problem. In the paper, we present a simple and effective method based on two geometrical characteristics constraints to trim the noisy points. One of the geometrical characteristics is the local density information and another is the deviation from the local fitting plane. The local density based method provides a preprocessing step, which could remove those sparse outlier and isolated outlier. The non-isolated outlier removal in this paper depends on a local projection method, which placing those points onto objects. There is no doubt that the deviation of any point from the local fitting plane should be a criterion to reduce the noisy points. The experimental results demonstrate the ability to remove the noisy point from various man-made objects consisting of complex outlier.
Xiaojuan Ning; Fan Li; Ge Tian; Yinghui Wang. An efficient outlier removal method for scattered point cloud data. PLOS ONE 2018, 13, e0201280 .
AMA StyleXiaojuan Ning, Fan Li, Ge Tian, Yinghui Wang. An efficient outlier removal method for scattered point cloud data. PLOS ONE. 2018; 13 (8):e0201280.
Chicago/Turabian StyleXiaojuan Ning; Fan Li; Ge Tian; Yinghui Wang. 2018. "An efficient outlier removal method for scattered point cloud data." PLOS ONE 13, no. 8: e0201280.
Sketch-drawing is one of the simplest and most direct means to illustrate 3-D objects. It can not only present geometric features, but also greatly facilitate us to identify and understand the object. This paper presents a simple yet effective tool to generate view-dependent sketch-drawing from point clouds. First, we extract the ridge-valley lines in geometric regions by utilizing their curvature; meanwhile, we extract the contours of point clouds based on the object view-dependent; the obtained ridge-valley lines and contours are optimized and fused to describe the geometric features of the object. Second, we apply the change models of line thickness diffusion based on angle and depth to finish the line thickness variation. Finally, the shadow region of sketch-drawing is achieved by using the shadow generation model based on line density and gray value. Experimental results show that our proposed method can effectively complete the object sketch-drawing simulation from point clouds. Furthermore, our method is more flexible and robust than the existing algorithms; it does not require the preprocessing on the input point clouds.
Yinghui Wang; Huanhuan Zhang; Xiaojuan Ning; Wen Hao; Zhenghao Shi; Minghua Zhao; Hongfang Zhou; Liansheng Sui; Ke Lv. Ridge-Valley-Guided Sketch-Drawing From Point Clouds. IEEE Access 2018, 6, 13697 -13705.
AMA StyleYinghui Wang, Huanhuan Zhang, Xiaojuan Ning, Wen Hao, Zhenghao Shi, Minghua Zhao, Hongfang Zhou, Liansheng Sui, Ke Lv. Ridge-Valley-Guided Sketch-Drawing From Point Clouds. IEEE Access. 2018; 6 ():13697-13705.
Chicago/Turabian StyleYinghui Wang; Huanhuan Zhang; Xiaojuan Ning; Wen Hao; Zhenghao Shi; Minghua Zhao; Hongfang Zhou; Liansheng Sui; Ke Lv. 2018. "Ridge-Valley-Guided Sketch-Drawing From Point Clouds." IEEE Access 6, no. : 13697-13705.
Land cover classification using very high spatial resolution (VHSR) imaging plays a very important role in remote sensing applications. However, image noise usually reduces the classification accuracy of VHSR images. Image spatial filters have been recently adopted to improve VHSR image land cover classification. In this study, a new object-based image filter using topology and feature constraints is proposed, where an object is considered as a central object and has irregular shapes and various numbers of neighbors depending on the nature of the surroundings. First, multi-scale segmentation is used to generate a homogeneous image object and extract the corresponding vectors. Then, topology and feature constraints are proposed to select the adjacent objects, which present similar materials to the central object. Third, the feature of the central object is smoothed by the average of the selected objects’ feature. This proposed approach is validated on three VHSR images, ranging from a fixed-wing aerial image to UAV images. The performance of the proposed approach is compared to a standard object-based approach (OO), object correlative index (OCI) spatial feature based method, a recursive filter (RF), and a rolling guided filter (RGF), and has shown a 6%–18% improvement in overall accuracy.
Zhiyong Lv; Wenzhong Shi; Jón Atli Benediktsson; Xiaojuan Ning. Novel Object-Based Filter for Improving Land-Cover Classification of Aerial Imagery with Very High Spatial Resolution. Remote Sensing 2016, 8, 1023 .
AMA StyleZhiyong Lv, Wenzhong Shi, Jón Atli Benediktsson, Xiaojuan Ning. Novel Object-Based Filter for Improving Land-Cover Classification of Aerial Imagery with Very High Spatial Resolution. Remote Sensing. 2016; 8 (12):1023.
Chicago/Turabian StyleZhiyong Lv; Wenzhong Shi; Jón Atli Benediktsson; Xiaojuan Ning. 2016. "Novel Object-Based Filter for Improving Land-Cover Classification of Aerial Imagery with Very High Spatial Resolution." Remote Sensing 8, no. 12: 1023.