| Road curb extraction from mobile LiDAR point clouds S Xu, R Wang, H Zheng IEEE Transactions on Geoscience and Remote Sensing 55 (2), 996-1009, 2016 | 98 | 2016 |
| A weighted one-class support vector machine F Zhu, J Yang, C Gao, S Xu, N Ye, T Yin Neurocomputing 189, 1-10, 2016 | 88 | 2016 |
| Recognizing street lighting poles from mobile LiDAR data H Zheng, R Wang, S Xu IEEE Transactions on Geoscience and Remote Sensing 55 (1), 407-420, 2016 | 72 | 2016 |
| Boundary detection and sample reduction for one-class support vector machines F Zhu, N Ye, W Yu, S Xu, G Li Neurocomputing 123, 166-173, 2014 | 70 | 2014 |
| Automatic extraction of street trees’ nonphotosynthetic components from MLS data S Xu, S Xu, N Ye, F Zhu International journal of applied earth observation and geoinformation 69, 64-77, 2018 | 58 | 2018 |
| Plane segmentation based on the optimal-vector-field in LiDAR point clouds S Xu, R Wang, H Wang, R Yang IEEE transactions on pattern analysis and machine intelligence 43 (11), 3991 …, 2020 | 44 | 2020 |
| A flexible architecture for extracting metro tunnel cross sections from terrestrial laser scanning point clouds Z Cao, D Chen, Y Shi, Z Zhang, F Jin, T Yun, S Xu, Z Kang, L Zhang Remote Sensing 11 (3), 297, 2019 | 44 | 2019 |
| 基于卷积神经网络的木材缺陷识别 徐姗姗， 刘应安， 徐昇 山东大学学报 (工学版) 43 (2), 23-28, 2013 | 43* | 2013 |
| A supervoxel approach to the segmentation of individual trees from LiDAR point clouds S Xu, N Ye, S Xu, F Zhu Remote Sensing Letters 9 (6), 515-523, 2018 | 39 | 2018 |
| An Optimal Hierarchical Clustering Approach to Mobile LiDAR Point Clouds X Sheng, R Wang, H Wang, H Zheng IEEE Transactions on Intelligent Transportation Systems, 2019 | 37 | 2019 |
| A new clustering-based framework to the stem estimation and growth fitting of street trees from mobile laser scanning data S Xu, X Sun, J Yun, H Wang IEEE Journal of Selected Topics in Applied Earth Observations and Remote …, 2020 | 32 | 2020 |
| Power Line Extraction From Mobile LiDAR Point Clouds S Xu, R Wang IEEE Journal of Selected Topics in Applied Earth Observations and Remote …, 2019 | 30 | 2019 |
| Finding the Samples near the Decision Plane for Support Vector Learning F Zhu, J Yang, J Gao, C Xu, S Xu, C Gao Information Sciences, 2016 | 26 | 2016 |
| Separation of wood and foliage for trees from ground point clouds using a novel least-cost path model S Xu, K Zhou, Y Sun, T Yun IEEE Journal of Selected Topics in Applied Earth Observations and Remote …, 2021 | 22 | 2021 |
| Individual stem detection in residential environments with MLS data S Xu, S Xu, N Ye, F Zhu Remote sensing letters 9 (1), 51-60, 2018 | 18 | 2018 |
| A new method for shoreline extraction from airborne LiDAR point clouds S Xu, N Ye, S Xu Remote Sensing Letters 10 (5), 496-505, 2019 | 17 | 2019 |
| Higher-order conditional random fields-based 3D semantic labeling of airborne laser-scanning point clouds Y Li, D Chen, X Du, S Xia, Y Wang, S Xu, Q Yang Remote Sensing 11 (10), 1248, 2019 | 16 | 2019 |
| Relative density degree induced boundary detection for one-class SVM F Zhu, J Yang, S Xu, C Gao, N Ye, T Yin Soft Computing, 1-13, 2015 | 15 | 2015 |
| Feasibility study on the estimation of the living vegetation volume of individual street trees using terrestrial laser scanning X Sun, S Xu, W Hua, J Tian, Y Xu Urban Forestry & Urban Greening 71, 127553, 2022 | 14 | 2022 |
| Classification of 3-D point clouds by a new augmentation convolutional neural network S Xu, X Zhou, W Ye, Q Ye IEEE Geoscience and Remote Sensing Letters 19, 1-5, 2022 | 14 | 2022 |
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