Computer Science ›› 2021, Vol. 48 ›› Issue (8): 169-174.doi: 10.11896/jsjkx.200500096

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Accurate Segmentation Method of Aerial Photography Buildings Based on Deep Convolutional Residual Network

XU Hua-jie1,2, ZHANG Chen-qiang1, SU Guo-shao3   

  1. 1 College of Computer and Electronic Information,Guangxi University,Nanning 530004,China;
    2 Guangxi Key Laboratory of Multimedia Communications and Network Technology,Nanning 530004,China;
    3 College of Civil Engineering and Architecture,Guangxi University,Nanning 530004,China
  • Received:2020-05-21 Revised:2020-10-23 Published:2021-08-10
  • About author:XU Hua-jie,born in 1974,Ph.D,associa-te professor,is a senior member of China Computer Federation.His main research interests include artificial intelligence,acoustic signal recognition and computer vision.
  • Supported by:
    Science and Technology Plan Project of Guangxi Zhuang Autonomous Region (2017AB15008),Science and Technology Plan Project of Chongzuo(FB2018001) and High Level Innovation Team and Outstanding Scholar Program of Universities in Guangxi Province.

Abstract: In order to solve the problems of high cost of obtaining the top plan view of the main outline of the building in the 3D modeling scenario,low segmentation accuracy of the aerial photography building,interference on the roof of the building,etc.,a method of accurately segmenting the aerial photography building based on deep residual network is proposed,in which the positions of five points are expressed as heat maps as additional input channels of the network,and good segmentation effect is achieved in the task of accurately segmenting the aerial photography building.Experimental results show that the proposedmethod has higher segmentation accuracy and segmentation efficiency than the traditional semi-automatic segmentation method Grabcut.It has better robustness and anti-interference than DEXTR method.This method can provide high-precision top-view contour map and top-view picture of buildings for 3D reconstruction of buildings,and can also be used in the production process of aerial photography building data sets as an accurate and effective mask annotation tool or semi-automatic contour annotation tool to improve the annotation efficiency of datasets.

Key words: 3D modeling, Aerial photography, Convolutional residual network, Heatmap, Image segmentation

CLC Number: 

  • TP391
[1]CORNELIS N,BASTIAN L,KURT C,et al.3D Urban Scene Modeling Integrating Recognition and[J].International Journal of Computer Vision,2008,78(2/3):121-141.
[2]SAHOO P K,SOLTANI S,WONG A K C.A survey of thre-sholding techniques[J].Computer Vision Graphics & Image Processing,1988,41(2):233-260.
[3]REUTER M,BIASOTTI S,GIORGI D,et al.Discrete Laplace-Beltrami operators for shape analysis and segmentation[J].Computers & Graphics,2009,33(3):381-390.
[4]ROTHER C,KOLMOGOROV V,BLAKE A.Grabcut:Interactive foreground extraction using iterated graph cuts[J].ACM Transactions on Graphics (TOG),ACM,2004,23(3):309-314.
[5]PAPADOPOULOS D P,UIJLINGS J R R,KELLER F,et al.Extreme clicking for efficient object annotation[C]//IEEE International Conference on Computer Vision.2017:4930-4939.
[6]MANINIS K K,CAELLES S,PONT-TUSET J,et al.Deep extreme cut:From extreme points to object segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2018:616-625.
[7]KRIZHEVSKY A,SUTSKEVER L,HINTON G E.ImageNetclassification with deep convolutional neural networks[C]//International Conference on Neural Information Processing Systems.Curran Associates Inc.2012:1097-1105.
[8]PAPANDREOU G,ZHU T,KANAZAWA N,et al.Towardsaccurate multi-person pose estimation in the wild[C]//Procee-dings of the IEEE Conference on Computer Vision and Pattern Recognition.2017:4903-4911.
[9]KHORBOTLY S,HASSAN F.A modified approximation of 2D Gaussian smoothing filters for fixed-point platforms[C]//the IEEE 43rd Southeastern Symposium on System Theory.IEEE,2011:151-159.
[10]HE K,ZHANG X,REN S,et al.Deep residual learning forimage recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2016:770-778.
[11]CHEN L C,PAPANDREOU G,KOKKINOS I,et al.DeepLab:Semantic Image Segmentation with Deep Convolutional Nets,Atrous Convolution,and Fully Connected CRFs[J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2018,40(4):834-848.
[12]ZHAO Z C,LUO Z,WANG P Y,et al.Survey on Image Classi-fication Algorithms Based on Deep Residual Network[J].Computer Systems & Applications,2020,29(1):14-21.
[13]YU F,KOLTUN V,FUNKHOUSER T.Dilated residual net-works[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2017:472-480.
[14]ZHAO H,SHI J,QI X,et al.Pyramid scene parsing network[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2017:2881-2890.
[15]TAN C,SUN F,KONG T,et al.A survey on deep transfer learning[C]//International Conference on Artificial Neural Networks.Cham:Springer,2018:270-279.
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