Optics and Precision Engineering, Volume. 32, Issue 24, 3658(2024)

Airborne point cloud classification integrating edge convolution and global-local self-attention

Jingmin TU1, Jin YAN1, Li LI2, Jian YAO1,2, Jie LI1、*, and Yanfei KANG3
Author Affiliations
  • 1Hubei Collaborative Innovation Center for High-efficiency Utilization of Solar Energy, Hubei University of Technology, Wuhan430068, China
  • 2School of Remote Sensing and Information Engineering, Wuhan University, Wuhan430079, China
  • 3Wuhan Survey and Design Co., LTD., Wuhan40020,China
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    References(46)

    [1] LUO B, YANG J, SONG S et al. Target classification of similar spatial characteristics in complex urban areas by using multispectral LiDAR[J]. Remote Sensing, 14, 238(2022).

    [2] ZHANG P X, WANG Q, GAO R J et al. Ground point cloud segmentation based on local threshold adaptive method[J]. Opt. Precision Eng., 31, 2564-2572(2023).

    [3] 徐婕, 刘慧, 沈跃. 基于改进PointNet++模型的苗圃树木点云分类与分割[J]. 中国激光, 51, 193-203(2024).

         XU J, LIU H, SHEN Y et al. Point clouds classification and segmentation for nursery trees based on improved PointNet++Model[J]. Chinese Journal of Lasers, 51, 193-203(2024).

    [4] KACPER J, WIOLETA P. Point cloud does matter. Selected issues of using airborne LiDAR elevation data in geomorphometric studies of rugged sandstone terrain under forest-Case study from Central Europe[J]. Geomorphology, 412, 108316(2022).

    [5] ZHOU K, LINDENBERGH R, GORTE B et al. LiDAR-guided dense matching for detecting changes and updating of buildings in Airborne LiDAR data[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 162, 200-213(2020).

    [6] DAI Z, HE R, WANG H T et al. Adaptive single tree extraction method based on fusion of airborne lidar and vegetation index[J]. Opt. Precision Eng., 31, 3331-3344(2023).

    [7] 龚建宗. 机载激光雷达在山区输电线路设计中的应用[J]. 测绘与空间地理信息, 47, 170-172(2024).

         GONG J Z. Application of airborne LiDAR data in transmission line design in mountainous area[J]. Geomatics & Spatial Information Technology, 47, 170-172(2024).

    [8] HORVÁTH E, POZNA C, UNGER M. Real-time LIDAR-based urban road and sidewalk detection for autonomous vehicles[J]. Sensors, 22, 194(2021).

    [9] 李峙含, 花海洋, 张浩. 基于对比学习的高光谱和LiDAR地物分类方法[J]. 激光与光电子学进展, 60, 3788(2023).

         LI Z H, HUA H Y, ZHANG H. Classification based on hyperspectral image and LiDAR data with contrastive learning[J]. Laser & Optoelectronics Progress, 60, 3788(2023).

    [10] TU J M, SHEN Y, LI J et al. On-board point cloud roof plane segmentation for voxel and point hybrid growth[J]. Chinese Journal of Lasers, 51, 2210002(2024).

         涂静敏, 沈阳, 李婕. 体素与点混合增长的机载点云屋顶平面分割[J]. 中国激光, 51, 2210002(2024).

    [11] LI J, LI Q Q, LI L et al. An airborne point cloud roof plane extraction algorithm based on deep learning[J]. Laser Technology, 48, 628(2024).

         李婕, 李青清, 李礼. 基于深度学习的机载点云屋顶平面提取算法[J]. 激光技术, 48, 628(2024).

    [12] ZHANG J X, LIN X G, NING X. SVM-based classification of segmented airborne LiDAR point clouds in urban areas[J]. Remote Sens, 5, 3749-3775(2013).

    [13] HE M Y, CHENG Y L, LIAO X J et al. Building extraction algorithm by fusing spectral and geometrical features[J]. Laser & Optoelectronics Progress, 55(2018).

    [14] MUNOZ D, VANDAPEL N, HEBERT M. Onboard contextual classification of 3-D point clouds with learned high-order markov random fields[C], 2009-2016(2009).

    [15] NIEMEYER J, ROTTENSTEINER F, SOERGEL U. Contextual classification of lidar data and building object detection in urban areas[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 87, 152-165(2014).

    [16] GENG X X. Research on LiDAR Point Cloud Multi-scale and Attentive Semantic Segmentation[D](2022).

         耿笑笑. LiDAR点云多尺度注意力语义分割方法研究[D](2022).

    [17] RABBANI T, VAN DEN HEUVEL F, VOSSELMANN G. Segmentation of point clouds using smoothness constraint[J]. International archives of photogrammetry, remote sensing and spatial information sciences, 36, 248-253(2006).

    [18] CHEN X Z, MA H M, WAN J et al. Multi-view 3D object detection network for autonomous driving[C], 6526-6534(2017).

    [19] CHARLES R Q, HAO S, MO K C et al. PointNet: deep learning on point sets for 3d classification and segmentation[C], 77-85(2017).

    [20] QI C, YI L, SU H et al. PointNet++: deep hierarchical feature learning on point sets in a metric space[J]. Advances in Neural Information Processing Systems, 5105-5114(2017).

    [21] LI D W, SHI G L, WU Y H et al. Multi-scale neighborhood feature extraction and aggregation for point cloud segmentation[J]. IEEE Transactions on Circuits and Systems for Video Technology, 31, 2175-2191(2021).

    [22] ZHAO H S, JIANG L, JIA J Y et al. Point transformer[C], 16239-16248(2021).

    [23] 徐田野, 丁海勇. 基于融合图卷积的深度学习点云分类方法[J]. 激光与光电子学进展, 59(2022).

         XU T Y, DING H Y. Deep learning point cloud classification method based on fusion graph convolution[J]. Laser & Optoelectronics Progress, 59(2022).

    [24] BAI B. Application analysis of airborne LiDAR in large-scale topographic mapping[J]. Geomatics & Spatial Information Technology, 47, 186-188, 192(2024).

         白斌. 机载LiDAR在大比例尺地形图测绘中的应用分析[J]. 测绘与空间地理信息, 47, 186-188, 192(2024).

    [25] ZAHEER M, GURUGANESH G, DUBEY K A et al. Big bird: Transformers for longer sequences[J]. Advances in neural information processing systems, 33, 17283-17297(2020).

    [26] LI J H, WEINMANN M, SUN X et al. VD-LAB: a view-decoupled network with local-global aggregation bridge for airborne laser scanning point cloud classification[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 186, 19-33(2022).

    [27] NIEMEYER J, ROTTENSTEINER F, SOERGEL U. Contextual classification of lidar data and building object detection in urban areas[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 87, 152-165(2014).

    [28] HAN X F, LIU C, ZHOU Y Z et al. WHU-Urban3D: an urban scene LiDAR point cloud dataset for semantic instance segmentation[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 209, 500-513(2024).

    [29] 刘盛, 曹益烽, 黄文豪. 融合稀疏注意力和实例增强的雷达点云分割[J]. 中国图象图形学报, 28, 483-494(2023).

         LIU S, CAO Y F, HUANG W H et al. LiDAR point cloud semantic segmentation combined with sparse attention and instance enhancement[J]. Journal of Image and Graphics, 28, 483-494(2023).

    [30] PHAN A V, LE NGUYEN M, NGUYEN Y et al. DGCNN: a convolutional neural network over large-scale labeled graphs[J]. Neural Networks, 108, 533-543(2018).

    [31] HORVAT D, ŽALIK B, MONGUS D. Context-dependent detection of non-linearly distributed points for vegetation classification in airborne LiDAR[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 116, 1-14(2016).

    [32] YANG Z S, JIANG W S, XU B et al. A convolutional neural network-based 3D semantic labeling method for ALS point clouds[J]. Remote Sensing, 9, 936(2017).

    [33] WANG S L, SUO S, MA W C et al. Deep parametric continuous convolutional neural networks[C], 2589-2597(2018).

    [34] YOUSEFHUSSIEN M, KELBE D J, IENTILUCCI E J et al. A multi-scale fully convolutional network for semantic labeling of 3D point clouds[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 143, 191-204(2018).

    [35] HU Q Y, YANG B, XIE L H et al. RandLA-net: efficient semantic segmentation of large-scale point clouds[C], 11105-11114(2020).

    [37] LAI X, LIU J H, JIANG L et al. Stratified transformer for 3D point cloud segmentation[C], 8490-8499(2022).

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    Jingmin TU, Jin YAN, Li LI, Jian YAO, Jie LI, Yanfei KANG. Airborne point cloud classification integrating edge convolution and global-local self-attention[J]. Optics and Precision Engineering, 2024, 32(24): 3658

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    Paper Information

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    Received: Jul. 8, 2024

    Accepted: --

    Published Online: Mar. 11, 2025

    The Author Email: Jie LI (jielonline@hbut.edu.cn)

    DOI:10.37188/OPE.20243224.3658

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