方差分析引导的高阶马尔可夫网络及其在点云建筑物提取中的应用
ANOVA guided high-order Markov network and its application in building extraction from point clouds
- 2023年27卷第4期 页码:1021-1033
纸质出版日期: 2023-04-07
DOI: 10.11834/jrs.20221421
扫 描 看 全 文
浏览全部资源
扫码关注微信
纸质出版日期: 2023-04-07 ,
扫 描 看 全 文
郝娇娇,倪欢,管海燕.2023.方差分析引导的高阶马尔可夫网络及其在点云建筑物提取中的应用.遥感学报,27(4): 1021-1033
Hao J J,Ni H and Guan H Y. 2023. ANOVA guided high-order Markov network and its application in building extraction from point clouds. National Remote Sensing Bulletin, 27(4):1021-1033
以ALS(Airborne Laser Scanning)系统为代表的三维点云获取技术为建筑物重建提供了一条高效便捷途径。本文基于ALS点云超体素,提出了一种方差分析引导的高阶马尔可夫网络,并用以提取建筑物。该方法以超体素作为无向图模型节点,根据三维邻域特有的局部几何属性,结合方差分析原理,生成高阶因子,并将特征转化为表达能力更强的节点和边势函数;再采用信念传播算法,对高阶马尔可夫网络进行推理,形成了一种非监督的建筑物识别框架。此外,本文采用由粗到精的识别策略,首先在独立假设前提下,利用贝叶斯高斯混合模型实现节点初始状态捕捉,再采用高阶马尔可夫网络对三维邻域的相关性建模,以提高建筑物提取精度。本文引入两组具备人工标记真值的开放ALS点云数据集进行实验,利用4种国际通用指标对建筑物提取结果进行精度评价。可视化分析表明,本文方法提取的建筑物内部完整,且边界清晰,为建筑物三维重建提供了可靠信息。定量化分析表明,在低层建筑主导的住宅区,本文方法的建筑物提取精度(基于投影面积和对象的
<math id="M1"><mi>F</mi><mn mathvariant="normal">1</mn></math>
http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=43332557&type=
http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=43332571&type=
3.55599999
2.28600001
指数)为95.4%和91.5%,均高于现有方法;在高层建筑主导的商业区,基于对象的
<math id="M2"><mi>F</mi><mn mathvariant="normal">1</mn></math>
http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=43332557&type=
http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=43332571&type=
3.55599999
2.28600001
指数达到93.5%,高于现有方法,基于投影面积的
<math id="M3"><mi>F</mi><mn mathvariant="normal">1</mn></math>
http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=43332557&type=
http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=43332571&type=
3.55599999
2.28600001
指数为92.9%,仍处于较高水平。
Building extraction is important for urban planning
land management
three-dimensional (3D) reconstruction
and other fields. Among various remote sensing techniques
3D point cloud acquisition technique represented by Airborne Laser Scanning (ALS) system provides an efficient and convenient way for building extraction and modeling. Currently
deep learning-based building extraction methods are widely used. Compared with them
unsupervised building extraction methods do not need to train with a large amount of manually labeled data and do not require powerful computation equipment. Thus
developing unsupervised building extraction methods that do not rely on manual labeling is greatly meaningful.
In this paper
based on the supervoxels of ALS point clouds
a high-order Markov network guided by the analysis of variance (ANOVA) is proposed for building extraction. This method first uses supervoxels as the nodes of the undirected graph
and then constructs high-order factors based on the principle of ANOVA and the local geometric features of 3D neighborhood. After that
the features are transformed into node and edge potential functions with a better expression ability. Finally
the belief propagation algorithm is used to make inference on the high-order Markov network
and an unsupervised building extraction framework is constructed.
In the experiments
two groups of ALS point cloud datasets with ground-truths are employed
and four commonly used metrics are utilized to evaluate the accuracy of the results. Visual analysis shows that our method extracts buildings with complete interiors and clear boundaries. Hence
this method can be used to provide reliable data for the 3D reconstruction of buildings. According to quantitative analysis
in residential areas dominated by low-rise buildings
the averaged accuracy (the projection-area-based and object-based F1 indexes) of our method reaches 95.4% and 91.5% which are higher than that of existing supervised and unsupervised methods. In downtown areas dominated by high-rise buildings
the averaged object-based F1 score of our method reaches 93.5% which is higher than that of existing methods; and its averaged projection-area-based F1 score gets 92.9%
which is more than sufficient.
This paper includes three innovative points. Firstly
an unsupervised building extraction framework from coarse to fine is constructed. Specifically
the Bayesian Gaussian Mixture Model is first used to capture the initial state of the node under the independent assumption
and then the high-order Markov network is designed to model the correlation of the 3D neighborhood for extracting buildings. Secondly
a potential function calculation method based on P-value test of ANOVA is constructed
which enhances the expression of interaction among supervoxels. Thirdly
higher-order factors are introduced into the supervoxel Markov network model
and the belief propagation algorithm is used for the inference of the network
thus the accurate identification of buildings is achieved. The results show that our method can effectively extract ALS point cloud buildings in different study areas
and the ablation study validates the positive effect of each module.
ALS贝叶斯高斯混合模型建筑物提取方差分析高阶马尔可夫网络
ALSGaussian Mixture Modelbuilding extractionANOVAhigh-order Markov network
Awrangjeb M and Fraser C S. 2014. Automatic segmentation of raw LiDAR data for extraction of building roofs. Remote Sensing, 6(5): 3716-3751 [DOI: 10.3390/rs6053716http://dx.doi.org/10.3390/rs6053716]
Buitinck L, Louppe G, Blondel M, Pedregosa F, Mueller A, Grisel O, Niculae V, Prettenhofer P, Gramfort A, Grobler J, Layton R, Vanderplas J, Joly A, Holt B and Varoquaux G. 2013. API design for machine learning software: experiences from the scikit-learn project. arXiv preprint arXiv:1309.0238
Chen C, Li X M, Belkacem A N, Qiao Z F, Dong E Z, Tan W J and Shin D. 2019. The mixed kernel function SVM-based point cloud classification. International Journal of Precision Engineering and Manufacturing, 20(5): 737-747 [DOI: 10.1007/s12541-019-00102-3http://dx.doi.org/10.1007/s12541-019-00102-3]
Chen S X, Shi W Z, Zhou M T, Zhang M and Chen P F. 2020. Automatic building extraction via adaptive iterative segmentation with LiDAR data and high spatial resolution imagery fusion. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13: 2081-2095 [DOI: 10.1109/JSTARS.2020.2992298http://dx.doi.org/10.1109/JSTARS.2020.2992298]
Cheng L, Zhao W, Han P, Zhang W, Shan J, Liu Y X and Li M C. 2013. Building region derivation from LiDAR data using a reversed iterative mathematic morphological algorithm. Optics Communications, 286: 244-250 [DOI: 10.1016/j.optcom.2012.08.028http://dx.doi.org/10.1016/j.optcom.2012.08.028]
Demantké J, Mallet C, David N and Vallet B. 2011. Dimensionality based scale selection in 3D LiDAR point clouds//International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Calgary: ISPRS: 97-102 [DOI: 10.5194/isprsarchives-XXXVIII-5-W12-97-2011http://dx.doi.org/10.5194/isprsarchives-XXXVIII-5-W12-97-2011]
Deng F, Dou A X and Wang X Q. 2018. Fusion of aerial imagery and airborne LiDAR data for post-earthquake building point extraction. Journal of Remote Sensing, 22(S1): 224-232
邓飞, 窦爱霞, 王晓青. 2018. 融合航空影像的震后机载LiDAR建筑物点云提取. 遥感学报, 22(S1): 228-236 [DOI: 10.11834/jrs.20187191http://dx.doi.org/10.11834/jrs.20187191]
Du S J, Zhang Y S, Zou Z R, Xu S H, He X and Chen S Y. 2017. Automatic building extraction from LiDAR data fusion of point and grid-based features. ISPRS Journal of Photogrammetry and Remote Sensing, 130: 294-307 [DOI: 10.1016/j.isprsjprs.2017.06.005http://dx.doi.org/10.1016/j.isprsjprs.2017.06.005]
Du S J, Zou Z R, Zhang Y S, He X and Wang J X. 2018. A building extraction method via graph cuts algorithm by fusion of LiDAR point cloud and orthoimage. Acta Geodaetica et Cartographica Sinica, 47(4): 519-527
杜守基, 邹峥嵘, 张云生, 何雪, 王竞雪. 2018. 融合LiDAR点云与正射影像的建筑物图割优化提取方法. 测绘学报, 47(4): 519-527 [DOI: 10.11947/j.AGCS.2018.20160534http://dx.doi.org/10.11947/j.AGCS.2018.20160534]
Gerke M and Xiao J. 2014. Fusion of airborne laserscanning point clouds and images for supervised and unsupervised scene classification. ISPRS Journal of Photogrammetry and Remote Sensing, 87: 78-92 [DOI: 10.1016/j.isprsjprs.2013.10.011http://dx.doi.org/10.1016/j.isprsjprs.2013.10.011]
Gilani S A N, Awrangjeb M and Lu G J. 2018. Segmentation of airborne point cloud data for automatic building roof extraction. GIScience & Remote Sensing, 55(1): 63-89 [DOI: 10.1080/15481603.2017.1361509http://dx.doi.org/10.1080/15481603.2017.1361509]
Guo F, Mao Z Y, Zou W B and Weng Q. 2020. A method for building extraction by fusing feature information from LiDAR data and high-resolution imagery. Journal of Geo-information Science, 22(8): 1654-1665
郭峰, 毛政元, 邹为彬, 翁谦. 2020. 融合LiDAR数据与高分影像特征信息的建筑物提取方法. 地球信息科学学报, 22(8): 1654-1665 [DOI: 10.12082/dqxxkx.2020.190459http://dx.doi.org/10.12082/dqxxkx.2020.190459]
Hazan T and Shashua A. 2010. Norm-product belief propagation: primal-dual message-passing for approximate inference. IEEE Transactions on Information Theory, 56(12): 6294-6316 [DOI: 10.1109/TIT.2010.2079014http://dx.doi.org/10.1109/TIT.2010.2079014]
Huang R G, Yang B S, Liang F X, Dai W X, Li J P, Tian M and Xu W X. 2018. A top-down strategy for buildings extraction from complex urban scenes using airborne LiDAR point clouds. Infrared Physics and Technology, 92: 203-218 [DOI: 10.1016/j.infrared.2018.05.021http://dx.doi.org/10.1016/j.infrared.2018.05.021]
Khoshelham K, Nardinocchi C, Frontoni E, Mancini A and Zingaretti P. 2010. Performance evaluation of automated approaches to building detection in multi-source aerial data. ISPRS Journal of Photogrammetry and Remote Sensing, 65(1): 123-133 [DOI: 10.1016/j.isprsjprs.2009.09.005http://dx.doi.org/10.1016/j.isprsjprs.2009.09.005]
Koller D and Friedman N. 2009. Probabilistic Graphical Models: Principles and Techniques. Cambridge: MIT Press: 588-642
Lai X D, Yang J R, Li Y X and Wang M W. 2019. A building extraction approach based on the fusion of LiDAR point cloud and elevation map texture features. Remote Sensing, 11(14): 1636 [DOI: 10.3390/rs11141636http://dx.doi.org/10.3390/rs11141636]
Lê-Huu D K. 2019. Nonconvex Alternating Direction Optimization for Graphs: Inference and Learning. Paris: Université Paris-Saclay: 1-10
Li D L, Shen X, Yu Y T, Guan H Y, Li J, Zhang G and Li D R. 2020. Building extraction from airborne multi-spectral LiDAR point clouds based on graph geometric moments convolutional neural networks. Remote Sensing, 12(19): 3186 [DOI: 10.3390/rs12193186http://dx.doi.org/10.3390/rs12193186]
Liu K, Ma H C, Ma H C, Cai Z and Zhang L. 2020. Building extraction from airborne LiDAR data based on min-cut and improved post-processing. Remote Sensing, 12(17): 2849 [DOI: 10.3390/rs12172849http://dx.doi.org/10.3390/rs12172849]
Maltezos E, Doulamis A, Doulamis N and Ioannidis C. 2019. Building extraction from LiDAR data applying deep convolutional neural networks. IEEE Geoscience and Remote Sensing Letters, 16(1): 155-159 [DOI: 10.1109/LGRS.2018.2867736http://dx.doi.org/10.1109/LGRS.2018.2867736]
Mongus D, Lukač N and Žalik B. 2014. Ground and building extraction from LiDAR data based on differential morphological profiles and locally fitted surfaces. ISPRS Journal of Photogrammetry and Remote Sensing, 93: 145-156 [DOI: 10.1016/j.isprsjprs.2013.12.002http://dx.doi.org/10.1016/j.isprsjprs.2013.12.002]
Mongus D, Lukač N, Obrul D and Žalik B. 2013. Detection of planar points for building extraction from LiDAR data based on differential morphological and attribute profiles//ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Regina: ISPRS: 21-26 [DOI: 10.5194/isprsannals-II-3-W1-21-2013http://dx.doi.org/10.5194/isprsannals-II-3-W1-21-2013]
Nguyen T H, Daniel S, Guériot D, Sintès C and Le Caillec J. 2020. Super-resolution-based snake model—An unsupervised method for large-scale building extraction using airborne LiDAR data and optical image. Remote Sensing, 12(11): 1702 [DOI: 10.3390/rs12111702http://dx.doi.org/10.3390/rs12111702]
Ni H and Niu X N. 2020. SVLA: A compact supervoxel segmentation method based on local allocation. ISPRS Journal of Photogrammetry and Remote Sensing, 163: 300-311 [DOI: 10.1016/j.isprsjprs.2020.03.011http://dx.doi.org/10.1016/j.isprsjprs.2020.03.011]
Niemeyer J, Rottensteiner F and Soergel U. 2012. Conditional random fields for lidar point cloud classification in complex urban areas. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Melbourne: ISPRS: 263-268 [DOI: 10.5194/isprsannals-I-3-263-2012http://dx.doi.org/10.5194/isprsannals-I-3-263-2012]
Niemeyer J, Rottensteiner F and Soergel U. 2013. Classification of urban LiDAR data using conditional random field and random forests//Joint Urban Remote Sensing Event 2013. Sao Paulo: IEEE: 139-142 [DOI: 10.1109/JURSE.2013.6550685http://dx.doi.org/10.1109/JURSE.2013.6550685]
Niemeyer J, Rottensteiner F and Soergel U. 2014. Contextual classification of lidar data and building object detection in urban areas. ISPRS Journal of Photogrammetry and Remote Sensing, 87: 152-165 [DOI: 10.1016/j.isprsjprs.2013.11.001http://dx.doi.org/10.1016/j.isprsjprs.2013.11.001]
Parmehr E G, Fraser C S, Zhang C S and Leach J. 2014. Automatic registration of optical imagery with 3D LiDAR data using statistical similarity. ISPRS Journal of Photogrammetry and Remote Sensing, 88: 28-40 [DOI: 10.1016/j.isprsjprs.2013.11.015http://dx.doi.org/10.1016/j.isprsjprs.2013.11.015]
Pulido-González N, García-Rodríguez S, Campo M, Rams J and Torres B. 2020. Application of DOE and ANOVA in optimization of HVOF spraying parameters in the development of new Ti coatings. Journal of Thermal Spray Technology, 29(3): 384-399 [DOI: 10.1007/s11666-020-00989-9http://dx.doi.org/10.1007/s11666-020-00989-9]
Rottensteiner F, Sohn G, Gerke M, Wegner J D, Breitkopf U and Jung J. 2014. Results of the ISPRS benchmark on urban object detection and 3D building reconstruction. ISPRS Journal of Photogrammetry and Remote Sensing, 93: 256-271 [DOI: 10.1016/j.isprsjprs.2013.10.004http://dx.doi.org/10.1016/j.isprsjprs.2013.10.004]
Tomljenovic I, Höfle B, Tiede D and Blaschke T. 2015. Building extraction from airborne laser scanning data: an analysis of the state of the art. Remote Sensing, 7(4): 3826-3862 [DOI: 10.3390/rs70403826http://dx.doi.org/10.3390/rs70403826]
Ullo S L, Zarro C, Wojtowicz K, Meoli G and Focareta M. 2020. LiDAR-based system and optical VHR data for building detection and mapping. Sensors, 20(5): 1285 [DOI: 10.3390/s20051285http://dx.doi.org/10.3390/s20051285]
Wang Y J, Jiang T P, Yu M, Tao S B, Sun J and Liu S. 2020. Semantic-based building extraction from LiDAR point clouds using contexts and optimization in complex environment. Sensors, 20(12): 3386 [DOI: 10.3390/s20123386http://dx.doi.org/10.3390/s20123386]
Wei Y Z, Yao W, Wu J W, Schmitt M and Stilla U. 2012. Adaboost-based feature relevance assessment in fusing LiDAR and image data for classification of trees and vehicles in urban scenes//ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Melbourne: ISPRS: 323-328 [DOI: 10.5194/isprsannals-I-7-323-2012http://dx.doi.org/10.5194/isprsannals-I-7-323-2012]
Widyaningrum E, Peters R Y and Lindenbergh R C. 2020. Building outline extraction from ALS point clouds using medial axis transform descriptors. Pattern Recognition, 106: 107447 [DOI: 10.1016/j.patcog.2020.107447http://dx.doi.org/10.1016/j.patcog.2020.107447]
Xu S J, Han J Q and Liu G H. 2013. Survey of image segmentation methods based on Markov random fields. Application Research of Computers, 30(9): 2576-2582
徐胜军,韩九强,刘光辉. 2013. 基于马尔可夫随机场的图像分割方法综述. 计算机应用研究, 30(9): 2576-2582 [DOI: 10.3969/j.issn.1001-3695.2013.09.004http://dx.doi.org/10.3969/j.issn.1001-3695.2013.09.004]
Zarea A and Mohammadzadeh A. 2016. A novel building and tree detection method from LiDAR data and aerial images. IEEE Journal of selected topics in applied Earth Observations and Remote Sensing, 9(5): 1864-1875 [DOI: 10.1109/JSTARS.2015.2470547http://dx.doi.org/10.1109/JSTARS.2015.2470547]
Zhang W M, Qi J B, Wan P, Wang H T, Xie D H, Wang X Y and Yan G J. 2016. An easy-to-use airborne LiDAR data filtering method based on cloth simulation. Remote Sensing, 8(6): 501 [DOI: 10.3390/rs8060501http://dx.doi.org/10.3390/rs8060501]
Zhao C, Guo H T, Lu J, Yu D X and Zhang B M. 2020. Airborne LiDAR point cloud classification based on deep residual network. Acta Geodaetica et Cartographica Sinica, 49(2): 202-213
赵传, 郭海涛, 卢俊, 余东行, 张保明. 2020. 基于深度残差网络的机载LiDAR点云分类. 测绘学报, 49(2): 202-213 [DOI: 10.11947/j.AGCS.2020.20190004http://dx.doi.org/10.11947/j.AGCS.2020.20190004]
Zhao C, Zhang B M, Chen X W, Guo H T and Lu J. 2017. Accurate and automatic building roof extraction using neighborhood information of point clouds. Acta Geodaetica et Cartographica Sinica, 46(9): 1123-1134
赵传, 张保明, 陈小卫, 郭海涛, 卢俊. 2017. 一种利用点云邻域信息的建筑物屋顶面高精度自动提取方法. 测绘学报, 46(9): 1123-1134 [DOI: 10.11947/j.AGCS.2017.20160518http://dx.doi.org/10.11947/j.AGCS.2017.20160518]
Zhao Q H, Li H Y and Li Y. 2015. Gaussian Mixture Model with variable components for full waveform LiDAR data decomposition and RJMCMC algorithm. Acta Geodaetica et Cartographica Sinica, 44(12): 1367-1377
赵泉华, 李红莹, 李玉. 2015. 全波形LiDAR数据分解的可变分量高斯混合模型及RJMCMC算法. 测绘学报, 44(12): 1367-1377 [DOI: 10.11947/j.AGCS.2015.20140501http://dx.doi.org/10.11947/j.AGCS.2015.20140501]
相关作者
相关机构