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

郝娇娇; 倪欢; 管海燕

doi:10.11834/jrs.20221421

模型与方法 | 浏览量 : 0 下载量: 1210 CSCD: 1

R-PDF
PDF
导出
分享
收藏
专辑

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
收稿：2021-06-18，

纸质出版：2023-04-07
DOI： 10.11834/jrs.20221421
稿件说明：

移动端阅览

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 DOI： 10.11834/jrs.20221421.

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 DOI： 10.11834/jrs.20221421.

摘要

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

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

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

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。

Abstract

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.

关键词

Keywords

references

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/rs6053716 http://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/s12541-019-00102-3 http://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.2020.2992298 http://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.2012.08.028 http://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-W12-97-2011 http://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.20187191 http://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.2017.06.005 http://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.2018.20160534 http://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.2013.10.011 http://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: 10.1080/15481603.2017.1361509 http://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.2020.190459 http://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.2010.2079014 http://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.2018.05.021 http://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.2009.09.005 http://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/rs11141636 http://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/rs12193186 http://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/rs12172849 http://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.2018.2867736 http://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.2013.12.002 http://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-W1-21-2013 http://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/rs12111702 http://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.2020.03.011 http://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-3-263-2012 http://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.2013.6550685 http://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.2013.11.001 http://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.2013.11.015 http://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/s11666-020-00989-9 http://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.2013.10.004 http://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/rs70403826 http://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/s20051285 http://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/s20123386 http://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-7-323-2012 http://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.2020.107447 http://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.1001-3695.2013.09.004 http://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.2015.2470547 http://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/rs8060501 http://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.2020.20190004 http://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.2017.20160518 http://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.2015.20140501 http://dx.doi.org/10.11947/j.AGCS.2015.20140501 ]

文章被引用时，请邮件提醒。

提交

集成主体边缘分离和多尺度信息提取的双分支建筑物提取网络

MFBFS：高分辨率多光谱遥感影像细粒度建筑物特征集

融合CNN与Transformer的高分辨率遥感影像建筑物双流提取模型

Res_ASPP_UNet++：结合分离卷积与空洞金字塔的遥感影像建筑物提取网络