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Remote Sensing for Natural Resources    2023, Vol. 35 Issue (3) : 88-96     DOI: 10.6046/zrzyyg.2022223
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Spatial-spectral joint classification of airborne multispectral LiDAR point clouds based on the multivariate GMM
WANG Liying1(), MA Xuwei2,3,4, YOU Ze1, WANG Shichao1, CAMARA Mahamadou1
1. School of Geomatics, Liaoning Technical University, Fuxin 123000, China
2. CGTEG China Coal Research Institute, Beijing 100013, China
3. Engineering Research Center for Technology Equipment of Emergency Refuge in Coal Mine, Beijing 100013, China
4. Beijing Coal Mine Safety Engineering Technology Research Center, Beijing 100013, China
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Abstract  

Conventional land cover classification methods based on airborne multispectral light detection and ranging (MS-LiDAR) data have insufficient capability for the cooperative utilization of spatial-spectral information or too high dimensions of features in the joint utilization of various features. This study proposed a spatial-spectral joint segmentation algorithm for airborne MS-LiDAR point clouds based on the multivariate Gaussian mixture model (GMM). First, radiometric correction, anomaly removal, and data fusion were performed for the original multi-band independent point clouds, forming multispectral point clouds that presented spatial locations and their multi-band spectral information. Then, spatial-spectral feature vectors were constructed using the extracted multispectral and elevation features of laser points. Meanwhile, the unit and scale differences among different types of features were eliminated through feature normalization and discretization. Subsequently, a GMM was built to fit the multimodal distribution of objects in the spatial-spectral feature space. Accordingly, the response levels of laser points to various objects were obtained, and the classification of various objects was determined according to the principle of maximum responsiveness. Finally, a 3D majority voting method was designed to optimize the segmentation results. The effectiveness and feasibility of the proposed algorithm were verified through experiments based on surveyed Optech Titan MS-LiDAR data. The experimental results show that the multivariate GMM combined with multi-band intensity features and elevation features yielded an overall segmentation accuracy of 93.57% and a Kappa coefficient of 0.912. The results also indicate that the high-accuracy segmentation of MS-LiDAR point clouds can be achieved by only combining four-dimensional features. This study provides a new approach for comprehensively utilizing the multispectral and spatial information in MS-LiDAR data.
Keywords multispectral light detection and ranging      point cloud segmentation      multivariate Gaussian mixture model      multimodal distribution      majority voting method      spatial-spectral joint feature     
ZTFLH:  TP79  
  P237  
Issue Date: 19 September 2023
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Articles by authors
Liying WANG
Xuwei MA
Ze YOU
Shichao WANG
Mahamadou CAMARA
Cite this article:   
Liying WANG,Xuwei MA,Ze YOU, et al. Spatial-spectral joint classification of airborne multispectral LiDAR point clouds based on the multivariate GMM[J]. Remote Sensing for Natural Resources, 2023, 35(3): 88-96.
URL:  
https://www.gtzyyg.com/EN/10.6046/zrzyyg.2022223     OR     https://www.gtzyyg.com/EN/Y2023/V35/I3/88
Fig.1  Top views of Titan MS-LiDAR intensity data
Fig.2  Probability density estimation in 2D feature space
Fig.3  Flowchart of the proposed algorithm
Fig.4  Top view of merged point cloud
序号 特征组合 总体精度/% Kappa系数
① IC1 + z 64.13 0.491
② IC2 + z 88.08 0.838
③ IC3 + z 65.45 0.509
④ I C 1+ IC2 + z 88.72 0.846
⑤ IC2 + IC3 +z 91.53 0.885
⑥ IC3 + IC1 +z 65.88 0.515
⑦ I C 1+ IC2 + IC3 + z 91.36 0.882
⑧ IC1 + IC2 + IC3 80.81 0.735
Tab.1  Comparison of segmentation accuracy under different feature combinations
指标 特征组合⑤ 特征组合⑦
优化前 优化后 优化前 优化后
总体精度/% 91.53 92.71 91.36 93.57
Kappa系数 0.885 0.901 0.882 0.912
Tab.2  Accuracy comparison of feature combination ⑤ and ⑦ before and after segmentation optimization
Fig.5  Segmentation results and comparison before and after optimization
实验数据 参考数据 合计 用户精度/%
建筑物 道路 树木 草地
建筑物 19 981 37 709 128 20 855 95.81
道路 0 23 623 134 2 264 26 021 90.78
树木 407 145 28 312 2 016 30 880 91.68
草地 24 936 873 39 785 41 618 95.60
合计 20 412 24 741 30 028 44 193 119 374
生产者精度/% 97.89 95.48 94.29 90.03
总体精度: 93.57% Kappa系数: 0.912
Tab.3  Confusion matrix for the proposed algorithm
算法参考文献 算法原理 联合特征 总体精度/% Kappa系数
本文算法 多元GMM 多光谱、nDSM 93.57 0.912
Huo等[10] SVM分类 多光谱、伪NDVIs、形态学剖面、多尺度形态学剖面、nDSM 93.28 0.910
Teo等[12] SVM分类 多光谱、NDFIs、曲率、nDSM 93.00 0.911
Zou等[21] 决策树分类 伪NDVI、绿化率、强度、高程、点数、返回数、类别、亮度、面积 91.63 0.895
Fernandez-Diaz等[8] 马氏距离 5个结构波段、2个波段强度 90.22 0.870
Ahokas等[15] 随机森林 多光谱、nDSM、点邻域特征 93.50 —
Shaker等[18] 对数似然分类 高程、高程变化、强度、强度变化、NDWI、回波数 96.50 —
Wang等[14] SVM分类 空间位置、多光谱、几何、结构 94.76 0.935
Tab.4  Accuracy comparison between the proposed algorithm and other classical algorithms
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