端到端堆叠沙漏网络的遥感影像建筑物轮廓重构

doi:10.13474/j.cnki.11-2246.2022.0143

测绘通报 ›› 2022, Vol. 0 ›› Issue (5): 67-73.doi: 10.13474/j.cnki.11-2246.2022.0143

端到端堆叠沙漏网络的遥感影像建筑物轮廓重构

张兴忆, 李佳田, 杨汝春, 陆大进, 张泽龙, 杨超

昆明理工大学国土资源工程学院, 云南昆明 650093

收稿日期:2021-05-19 修回日期:2022-02-24 发布日期:2022-06-08
作者简介:张兴忆(1997-),女,硕士生,研究方向为计算机视觉。E-mail:1823460533@qq.com通信简介:李佳田。E-mail:ljtcx@163.com
基金资助:
国家自然科学基金(41561082)

Reconstruction of remote sensing image building contour based on end-to-end stacked hourglass network

ZHANG Xingyi, LI Jiatian, YANG Ruchun, LU Dajin, ZHANG Zelong, YANG Chao

Faculty of Land Resource Engineering, Kunming University of Science and Technology, Kunming 650093, China

Received:2021-05-19 Revised:2022-02-24 Published:2022-06-08

摘要/Abstract

摘要： 针对如何以端到端可训练的方式重构建筑物轮廓的问题,本文提出了一种端到端多残差模块堆叠沙漏网络的建筑物轮廓重构方法。首先,采用多残差模块堆叠沙漏网络提取建筑物角点和边缘特征;其次,利用角点检测模块匹配对应角点的相对位置,以获取候选角点;然后,通过线段采样将候选角点生成候选轮廓线;最后,线验证模块利用候选线段及多残差模块堆叠沙漏网络得到特征图,并验证每个线段是否为建筑物轮廓线,以获得建筑物轮廓重构结果。试验结果表明,在SpaceNet建筑物数据集上,本文方法能检测出建筑物角点及边缘,并有效实现了以端到端可训练的方式重构建筑物轮廓。

关键词: 建筑物轮廓重构, 堆叠沙漏网络, 残差模块, 角点检测, 线段采样

Abstract: Considering the problem that how to reconstruct building contour in an end-to-end trainable way, a method of building contour reconstruction based on end-to-end and multi-residual module stacked hourglass network is proposed. Firstly, the multi-residual module stacked hourglass network is used to extract features of buildings' corners and edges. Secondly, the corners detection module is used to match the relative positions of the corresponding corners to obtain candidate corners, and then the candidate corners are sampled by line sampling module to generate candidate contour lines. Finally, the line verification module used candidate contour lines and feature map obtained by stacked hourglass network verify whether each line segment is a building contour, so as to obtain the result of building contour reconstruction. Experimental results show that on SpaceNet building data set, this method can detect the corners and edges of buildings, and effectively reconstruct the building contour in an end-to-end trainable way.

Key words: building contour reconstruction, stacked hourglass network, residual module, corner detection, line segment sampling

中图分类号:

P237

张兴忆, 李佳田, 杨汝春, 陆大进, 张泽龙, 杨超. 端到端堆叠沙漏网络的遥感影像建筑物轮廓重构[J]. 测绘通报, 2022, 0(5): 67-73.

ZHANG Xingyi, LI Jiatian, YANG Ruchun, LU Dajin, ZHANG Zelong, YANG Chao. Reconstruction of remote sensing image building contour based on end-to-end stacked hourglass network[J]. Bulletin of Surveying and Mapping, 2022, 0(5): 67-73.

参考文献

[1] 季顺平,魏世清.遥感影像建筑物提取的卷积神经元网络与开源数据集方法[J].测绘学报, 2019, 48(4):448-459.
[2] 常京新,高贤君,杨元维,等.融合多时相高分影像的建筑物轮廓优化方法[J].测绘通报, 2020(7):112-115.
[3] NORONHA S,NEVATIA R.Detection and modeling of buildings from multiple aerial images[J].IEEE Transactions on Pattern Analysis and Machine Intelligence, 2001, 23(5):501-518.
[4] 杨化超,邓喀中,张书毕.基于Hough变换的航空影像建筑物半自动提取[J].测绘科学, 2006, 31(6):93-94, 97.
[5] 孙金彦,黄祚继,周绍光,等.高分辨率遥感影像中建筑物轮廓信息矢量化[J].遥感学报, 2017, 21(3):396-405.
[6] LOKHAT I, TOUYA G. Enhancing building footprints with squaring operations[J]. Journal of Spatial Information Science, 2016, 2016(13):33-60.
[7] 尹烁,闫小明,晏雄锋.基于特征边重构的建筑物化简方法[J].测绘学报, 2020, 49(6):703-710.
[8] MARCOS D, TUIA D, KELLENBERGER B, et al. Learning deep structured active contours end-to-end[C]//Proceedings of 2018 IEEE Conference on Computer Vision and Pattern Recognition. Salt Lake City:IEEE, 2018:8877-8885.
[9] CHENG D, LIAO R, FIDLER S, et al. DARNet:deep active ray network for building segmentation[C]//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach:IEEE, 2019:7431-7439.
[10] ZHANG Z, LI Z, BI N, et al.PPGNet:learning point-pair graph for line segment detection[C]//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition.Long Beach:IEEE,2019:7105-7114.
[11] CHEN J, LIU C, WU J, et al. Floor-SP:inverse CAD for floorplans by sequential room-wise shortest path[C]//Proceedings of 2019 IEEE International Conference on Computer Vision. Seoul, Korea (South):IEEE,2019:2661-2670.
[12] COUGHLAN J M, YUILLE A L. Manhattan World:compass direction from a single image by Bayesian inference[C]//Proceedings of the 7th IEEE International Conference on Computer Vision.Kerkyra:IEEE, 1999:941-947.
[13] NAUATA N, FURUKAWA Y. Vectorizing world buildings:planar graph reconstruction by primitive detection and relationship inference[C]//Proceedings of 2020 European Conference on Computer Vision. Cham:Springer, 2020:711-726.
[14] NEWELL A, YANG K, DENG J. Stacked hourglass networks for human pose estimation[C]//Proceedings of 2016 European Conference on Computer Vision. Cham:Springer, 2016:483-499.
[15] 邹昆,王伟灿,董帅,等.基于堆叠沙漏网络的量体特征点定位[J].电子科技大学学报, 2020, 49(5):709-717.
[16] HE K, ZHANG X, REN S, et al. Deep residual learning for image recognition[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas:IEEE, 2016:770-778.
[17] TOMPSON J, GOROSHIN R, JAIN A, et al. Efficient object localization using Convolutional Networks[C]//Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition.Boston:IEEE,2015:648-656.
[18] LONG J,SHELHAMER E,DARRELL T.Fully convolutional networks for semantic segmentation[C]//Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition.Boston:IEEE, 2015:3431-3440.
[19] ZHOU Y, QI H, MA Y. End-to-end wireframe parsing[C]//Proceedings of 2019 IEEE International Conference on Computer Vision. Seoul, Korea (South):IEEE, 2019:962-971.
[20] GIRSHICK R. Fast R-CNN[C]//Proceedings of 2015 IEEE International Conference on Computer Vision. Santiago:IEEE, 2015:1440-1448.
[21] HE K, GKIOXARI G, DOLLAR P, et al. Mask R-CNN[C]//Proceedings of 2017 IEEE International Conference on Computer Vision.Venice:IEEE, 2017:2980-2988.
[22] REN S Q, HE K M, GIRSHICK R, et al. Faster R-CNN:towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(6):1137-1149.
[23] ACUNA D,LING H, KAR A,et al.Efficient interactive annotation of segmentation datasets with polygon-RNN++[C]//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.Salt Lake City:IEEE,2018:859-868.
[24] VAN E A,LINDENBAUM D,BACASTOW T M.SpaceNet:a remote sensing dataset and challenge series[EB/OL].[2021-05-10].https://arxiv.org/pdf/1807.01232.pdf.

端到端堆叠沙漏网络的遥感影像建筑物轮廓重构

Reconstruction of remote sensing image building contour based on end-to-end stacked hourglass network

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 3

编辑推荐

Metrics

本文评价

[1]	王杰茜, 冯德俊, 陈建飞. 对比Harris算子和Susan算子的建筑物边界规则化方法[J]. 测绘通报, 2020, 0(4): 11-15.
[2]	张永宏, 蔡朋艳, 陶润喆, 王剑庚, 田伟. 基于改进U-Net网络的遥感图像云检测[J]. 测绘通报, 2020, 0(3): 17-20,34.
[3]	李灵芝, 李百寿, 沈宇臻, 许锐. 一种基于SVM的无人机影像中单个建筑物的角点检测方法[J]. 测绘通报, 2017, 0(10): 52-57,78.