Graph convolution neural network method for shape classification of areal settlements

doi:10.11947/j.AGCS.2022.20210134

Abstract

Abstract: Shape recognition and classification is one of the important contents of cartographic generalization. Areal settlement is an important part of geospatial vector data and its shape cognition is a basic technique of cartographic generalization. To solve the shortcomings of traditional geometric and statistical shape classification methods, this paper proposes a novel areal settlements shape classification method based on graph data classification ability of graph convolutional neural network. Firstly, the computation graph is generated according to the contour polygon of areal settlement, and the features of the contour shape are extracted as the attributes of the vertices of computation graph. Secondly, the vertex attributes of the computation graph are aggregated and transmitted for multiple rounds, and the shape information is embedded into a high dimension vector with these vertices attributes. Finally, the graph vectors are input into a fully connected neural network to realize the classification of graphs. The experimental results show that this method can effectively achieve the end-to-end shape information extraction and classification of areal settlements. And it overcomes the deficiency of setting parameters through experience in traditional methods.

Key words: areal settlement, graph convolutional neural network, shape classification, cartographic generalization, graph classification

CLC Number:

P208

YU Yangyang, HE Kangjie, WU Fang, XU Junkui. Graph convolution neural network method for shape classification of areal settlements[J]. Acta Geodaetica et Cartographica Sinica, 2022, 51(11): 2390-2402.

References

[1] STEINIGER S, LANGE T, BURGHARDT D, et al. An approach for the classification of urban building structures based on discriminant analysis techniques[J]. Transactions in GIS, 2008, 12(1): 31-59.
[2] 胡慧明,钱海忠,何海威,等. 采用层次分析法的面状居民地自动选取[J]. 测绘学报,2016,45(6):740-746.DOI:10.11947 /j.AGCS.2016.20150078. HU Huiming, QIAN Haizhong, HE Haiwei,et al. Auto-selection of areal habitation based on analytic hierarchy process[J]. Acta Geodaetica et Cartographica Sinica,2016,45(6);740-746. DOI:10.11947/j.AGCS.2016.20150078.
[3] 黄宝群, 盛业华, 郭宁宁, 等. 同名边界点的面状居民地要素匹配[J]. 测绘科学, 2018, 43(2): 108-113. HUANG Baoqun, SHENG Yehua, GUO Ningning, et al. Residential polygon features matching based on identical boundary points[J]. Science of Surveying and Mapping, 2018, 43(2): 108-113.
[4] 张桥平, 李德仁, 龚健雅. 城市地图数据库面实体匹配技术[J]. 遥感学报, 2004, 8(2): 107-112. ZHANG Qiaoping, LI Deren, GONG Jianya. Areal feature matching among urban geographic databases[J]. Journal of Remote Sensing, 2004, 8(2): 107-112.
[5] LINDSEY D T. Vision science: photons to phenomenology[J]. Optometry and Vision Science, 2000, 77(5): 233-234.
[6] 艾廷华, 帅赟, 李精忠. 基于形状相似性识别的空间查询[J]. 测绘学报, 2009, 38(4): 356-362. AI Tinghua, SHUAI Yun, LI Jingzhong. A spatial query based on shape similarity cognition[J]. Acta Geodaetica et Cartographica Sinica, 2009, 38(4): 356-362.
[7] AI Tinghua, CHENG Xiaoqiang, LIU Pengcheng, et al. A shape analysis and template matching of building features by the Fourier transform method[J]. Computers, Environment and Urban Systems, 2013, 41: 219-233.
[8] 程绵绵,孙群,徐立,等.面轮廓线相似性和复杂性度量及在化简中的应用[J].测绘学报,2019,48(4):489-501. DOI:10.11947/j.AGCS.2019.20180124. CHENG Mianmian, SUN Qun, XU Li, et al, Polygon contour similarity and complexity measurement and application in simplification[J]. Acta Geodaetica et Cartographica Sinica, 2019, 48(4):489-501. DOI:10.11947/j.AGCS.2019.20180124.
[9] 晏雄锋,艾廷华,杨敏. 居民地要素化简的形状识别与模板匹配方法[J]. 测绘学报,2016,45(7):874-882. DOI:10.11947/j.AGCS.2016.20150162. YAN Xiongfeng, AI Tinghua, YANG Min. A simplification of residential feature by the shape cognition and template matching method[J]. Acta Geodaetica et Cartographica Sinica, 2016,45(7):874-882. DOI:10.11947/j.AGCS.2016.20150162.
[10] YONG K L, ŽALIK B. An efficient chain code with Huffman coding[J]. Pattern Recognition, 2005, 38(4): 553-557.
[11] PETER A M, RANGARAJAN A. Maximum likelihood wavelet density estimation with applications to image and shape matching[J]. IEEE Transactions on Image Processing, 2008, 17(4): 458-468.
[12] BELONGIE S, MALIK J, PUZICHA J. Shape matching and object recognition using shape contexts[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(4): 509-522.
[13] SAAVEDRA J M. Sketch based image retrieval using a soft computation of the histogram of edge local orientations (S-HELO)[C]//Proceedings of 2014 IEEE International Conference on Image Processing. Paris, France:IEEE, 2014: 2998-3002.
[14] KRIZHEVSKY A, SUTSKEVER I, HINTON G E. ImageNet classification with deep convolutional neural networks[J]. Communications of the ACM, 2017, 60(6): 84-90.
[15] KIM Y. Convolutional neural networks for sentence classification[C]//Proceedings of 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). Stroudsburg, PA, USA: Association for Computational Linguistics, 2014: 1746-1751.
[16] ABDEL-HAMID O, MOHAMED A R, JIANG Hui, et al. Convolutional neural networks for speech recognition[J]. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2014, 22(10): 1533-1545.
[17] SUN Long, WU Tao, SUN Guangcai, et al. Object detection research of SAR image using improved faster region-based convolutional neural network[J]. Journal of Geodesy and Geoinformation Science, 2020, 3(3): 18-28. DOI:10.11947/j.JGGS.2020.0302.
[18] ZUO Zongcheng, ZHANG Wen, ZHANG Dongying. A remote sensing image semantic segmentation method by combining deformable convolution with conditional random fields[J]. Acta Geodaetica et Cartographica Sinica, 2019, 48(6): 718-726.
[19] GONG Jianya, JI Shunping. Photogrammetry and deep learning[J]. Journal of Geodesy and Geoinformation Science, 2018, 1(1): 1-15. DOI: 10.11947/j.JGGS.2018.0101.
[20] 何海威,钱海忠,谢丽敏,等.立交桥识别的CNN卷积神经网络法[J].测绘学报,2018,47(3):385-395. DOI:10.11947/j.AGCS.2018.20170265. HE Haiwei,QIAN Haizhong,XIE Limin, et al. Interchange recognition method based on CNN[J]. Acta Geodaetica et Cartographica Sinica, 2018,47(3):385-395.DOI:10.11947/j.AGCS.2018.20170265.
[21] 张鸿刚, 李成名, 武鹏达, 等. GoogLeNet神经网络的复杂交叉路口识别方法[J]. 测绘科学, 2020, 45(10): 190-197. ZHANG Honggang, LI Chengming, WU Pengda, et al. A complex junction recognition method based on GoogleNet model[J]. Science of Surveying and Mapping, 2020, 45(10): 190-197.
[22] 马磊, 闫浩文, 王中辉, 等. 机器自监督学习的建筑物面要素几何形状度量[J]. 测绘科学, 2017, 42(12): 171-177. MA Lei, YAN Haowen, WANG Zhonghui, et al. Geometry shape measurement of building surface elements based on self-supervised machine learning[J]. Science of Surveying and Mapping, 2017, 42(12): 171-177.
[23] YAN Xiongfeng, AI Tinghua, YANG Min, et al. A graph convolutional neural network for classification of building patterns using spatial vector data[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2019, 150: 259-273.
[24] YAN Xiongfeng, AI Tinghua, YANG Min, et al. Graph convolutional autoencoder model for the shape coding and cognition of buildings in maps[J]. International Journal of Geographical Information Science, 2021, 35(3): 490-512.
[25] 徐冰冰, 岑科廷, 黄俊杰, 等. 图卷积神经网络综述[J]. 计算机学报, 2020, 43(5): 755-780. XU Bingbing, CEN Keting, HUANG Junjie, et al. A survey on graph convolutional neural network[J]. Chinese Journal of Computers, 2020, 43(5): 755-780.
[26] SHUMAN D I, NARANG S K, FROSSARD P, et al. The emerging field of signal processing on graphs: extending high-dimensional data analysis to networks and other irregular domains[J]. IEEE Signal Processing Magazine, 2013, 30(3): 83-98.
[27] MICHELI A. Neural network for graphs: a contextual constructive approach[J]. IEEE Transactions on Neural Networks, 2009, 20(3): 498-511.
[28] ATWOOD J, TOWSLEY D. Diffusion-Convolutional neural networks[EB/OL]. (2021-12-19).https//arXiv:1511.02136,2015.
[29] MONTI F, BOSCAINI D, MASCI J, et al. Geometric deep learning on graphs and manifolds using mixture model CNNs[C]//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition.Honolulu, HI, USA: IEEE, 2017: 5425-5434.
[30] KIPF T N,WELLING M. Semi-supervised classification with graph convolutional networks [EB/OL].(2021-12-19).https//arxiv. org/abs1609. 02907,2016.
[31] GLOROT X,BENGIO Y. Understanding the difficulty of training deep feed forward neural networks[C]//Proceedings of the 13th International Conference on Artificial Intelligence and Statistics.Sardinia,Italy:[s.n.],2010.
[32] YAN Xiongfeng, AI Tinghua, ZHANG Xiang. Template matching and simplification method for building features based on shape cognition[J]. ISPRS International Journal of Geo-Information, 2017, 6(8): 250.