结合边界约束网络和分水岭分割算法的建筑物提取

罗壮; 李明; 张德朝

doi:10.11834/jrs.20219335

遥感技术方法 | 浏览量 : 0 下载量: 1403 CSCD: 5

R-PDF
PDF
导出
分享
收藏
专辑

结合边界约束网络和分水岭分割算法的建筑物提取
Building detection based on a boundary-regulated network and watershed segmentation
2022年26卷第7期页码：1459-1468
收稿：2019-09-25，

纸质出版：2022-07-07
DOI： 10.11834/jrs.20219335
稿件说明：

移动端阅览

罗壮，李明，张德朝.2022.结合边界约束网络和分水岭分割算法的建筑物提取.遥感学报，26（7）： 1459-1468 DOI： 10.11834/jrs.20219335.

Luo Z，Li M and Zhang D Z. 2022. Building detection based on a boundary-regulated network and watershed segmentation. National Remote Sensing Bulletin， 26（7）：1459-1468 DOI： 10.11834/jrs.20219335.

摘要

城市作为高密度建筑区域，在较小范围内有大量结构相似的建筑紧密分布。当前从高分辨率图像中准确检测建筑仍然是一个挑战，本文受边缘检测网络启发，提出一种强化边界精度的建筑物提取新方案，根据建筑物及边界特点改进深度网络，结合自下而上分组的分水岭分割提高分类精度和建筑边界的准确度。首先对数据预处理，生成建筑边界和建筑分割线两类辅助标签；改进性能较优的建筑检测框架ICT-Net网络，修改网络结构和损失函数，针对两类辅助标签，强化边界影响，提高网络性能；最后对网络预测结果应用结合分水岭分割和梯度提升回归树的后处理，实现高精度的建筑提取。结果表明，数据预处理、改进深度学习算法可提高建筑检测像素精度IOU（Intersection over Union）约1%。后处理能充分利用网络输出的概率信息，有效优化建筑边界，在网络预测结果的基础上提高建筑实例召回率10.5%。本文方案与原始的ICT-Net网络相比，提高建筑实例召回率22.9%。

Abstract

High-density urban cities contain numerous similar buildings positioned in close proximity. Building detection from high-spatial-resolution remote sensing imagery in such scenes remains a challenge in computer vision and remote sensing urban applications. The integration of traditional segmentation algorithms and a novel neural network is an effective approach for such challenging settings. Inspired by the recent success of deep-learning-based edge detection

a new building detection method aiming at accurate boundaries is proposed. In accordance with the characteristics of buildings and their border

this study improves the network structure and integrates the network with bottom-up watershed segmentation to improve boundary precision and classification accuracy.

First

two auxiliary labels

namely

the building boundary and parting line

are derived from the original dataset through data preprocessing. Second

the newly proposed building detection frame called ICT-Net is improved by modifying its structure and loss function in accordance with the two auxiliary labels to obtain the probability of three classes. Lastly

a post-process integrating watershed segmentation with gradient-boosted regression trees is employed to achieve high-accuracy building detection. Specifically

a probability feature map is generated by merging the probability of three classes. Watershed segmentation with building marker thresholds is applied to obtain building instances from the probability feature map. Then

the building probability of each building instance predicted by gradient-boosted regression trees is used to select building instances

resulting in building detection results. Parameter selection is also implemented.

The performance of the proposed method is validated on the INRIA dataset

which provides aerial orthorectified color imagery with a spatial resolution of 0.3 m and with corresponding ground truth labels for two semantic classes: building and not building. Experimental results suggest that data preprocessing and the application of boundary loss can obtain an improvement of 1% in terms of the Intersection over Union (IOU) of building detection. The post-process can take full advantage of probability information from the network

thereby effectively optimizing the building boundary. The post-process brings an improvement of 10.5% in terms of building instance recall compared with the results of the neural network. Our study achieves a building instance recall rate that is 22.9% higher than that of the original ICT-Net.

A novel building detection method based on a boundary-regulated network and watershed segmentation is proposed in this study. Experimental results reveal the advantages of the enhanced-boundary-oriented data preprocessing and modified neural network and demonstrate that the proposed method can further improve prediction accuracy on a network basis. However

the excellent performance of the proposed method largely depends on parameter selection

and further improvements should be made in the future.

关键词

Keywords

references

Bokhovkin A and Burnaev E . 2019 . Boundary loss for remote sensing imagery semantic segmentation // Proceedings of the 16th International Symposium on Neural Networks . Moscow, Russia : Springer: 388 - 401 [ DOI: 10.1007/978-3-030-22808-8_38 http://dx.doi.org/10.1007/978-3-030-22808-8_38 ]

Chatterjee B and Poullis C . 2019 . On building classification from remote sensor imagery using deep neural networks and the relation between classification and reconstruction accuracy using border localization as proxy // Proceedings of the 16th Conference on Computer and Robot Vision . Kingston, ON, Canada : IEEE: 41 - 48 [ DOI: 10.1109/CRV.2019.00014 http://dx.doi.org/10.1109/CRV.2019.00014 ]

Chollet F. 2015 . Keras . https://github.com/fchollet/keras https://github.com/fchollet/keras

Demir I , Koperski K , Lindenbaum D , Pang G , Huang J , Basu S , Hughes F , Tuia D and Raska R . 2018 . DeepGlobe 2018: a challenge to parse the earth through satellite images // Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops . Salt Lake City, Utah : IEEE: 172 - 17209 [ DOI: 10.1109/CVPRW.2018.00031 http://dx.doi.org/10.1109/CVPRW.2018.00031 ]

Dosovitskiy A , Fischer P , Ilg E , Häusser P , Hazirbas C , Golkov V , Van Der Smagt P , Cremers D and Brox T . 2015 . FlowNet: learning optical flow with convolutional networks . Proceedings of 2015 IEEE International Conference on Computer Vision. Santiago, Chile : IEEE : 2758 - 2766 [ DOI: 10.1109/ICCV.2015.316 http://dx.doi.org/10.1109/ICCV.2015.316 ]

Dozat T . 2016 . Incorporating nesterov momentum into Adam [EB/OL]. Proceedings of International Conference on Learning Representations Workshop ( 1 ): 2013 -2016[ 2019-09-20 ]. https://openreview.net/pdf?id=OM0jvwB8jIp57ZJjtNEZ https://openreview.net/pdf?id=OM0jvwB8jIp57ZJjtNEZ

Goldman E , Herzig R , Eisenschtat A , Goldberger J and Hassner T . 2019 . Precise detection in densely packed scenes // Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Long Beach, CA, USA : IEEE: 5222 - 5233 [ DOI: 10.1109/CVPR.2019.00537 http://dx.doi.org/10.1109/CVPR.2019.00537 ]

He K M , Gkioxari G , Dollár P and Girshick R . 2017 . Mask R-CNN . Proceedings of the IEEE International Conference on Computer Vision . Venice, Italy : IEEE : 2980 - 2988 [ DOI: 10.1109/ICCV.2017.322 http://dx.doi.org/10.1109/ICCV.2017.322 ]

Hosang J , Benenson R , Dollár P and Schiele B . 2016 . What makes for effective detection proposals? IEEE Transactions on Pattern Analysis and Machine Intelligence , 38 ( 4 ): 814 - 830 [ DOI: 10.1109/TPAMI.2015.2465908 http://dx.doi.org/10.1109/TPAMI.2015.2465908 ]

Iglovikov V , Mushinskiy S and Osin V . 2017 . Satellite imagery feature detection using deep convolutional neural network: a kaggle competition [J/OL]. arXiv preprint arXiv : 1706 . 06169 [ 2019-09-20 ]. https://arxiv.org/pdf/1706.06169.pdf https://arxiv.org/pdf/1706.06169.pdf

Jégou S , Drozdzal M , Vazquez D , Romero A and Bengio Y . 2017 . The one hundred layers tiramisu: fully convolutional densenets for semantic segmentation . Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops. Honolulu, HI, USA : IEEE : 1175 - 1183 [ DOI: 10.1109/CVPRW.2017.156 http://dx.doi.org/10.1109/CVPRW.2017.156 ]

Ke G L , Meng Q , Finley T , Wang T F , Chen W , Ma W D , Ye Q W and Liu T Y . 2017 . LightGBM: a highly efficient gradient boosting decision tree[C/OL]. Proceedings of Advances in Neural Information Processing Systems. Long Beach, California, USA . Curran Associates Inc : 3146-3154[2019-09-20] . https://papers.nips.cc/paper/6907-lightgbm-a-highly-efficient-gradient-boosting-decision-tree.pdf https://papers.nips.cc/paper/6907-lightgbm-a-highly-efficient-gradient-boosting-decision-tree.pdf

Lin T Y , Goyal P , Girshick R , He K M and Dollár P . 2017 . Focal loss for dense object detection . Proceedings of the IEEE International Conference on Computer Vision . Venice, Italy : IEEE : 2999 - 3007 [ DOI: 10.1109/ICCV.2017.324 http://dx.doi.org/10.1109/ICCV.2017.324 ]

Liow Y T and Pavlidis T . 1990 . Use of shadows for extracting buildings in aerial images . Computer Vision, Graphics, and Image Processing , 49 ( 2 ): 242 - 277 [ DOI: 10.1016/0734-189X(90)90139-M http://dx.doi.org/10.1016/0734-189X(90)90139-M ]

Liu Z J , Wang J and Liu W P . 2005 . Building extraction from high resolution imagery based on multi-scale object oriented classification and probabilistic Hough transform // Proceedings of 2005 IEEE International Geoscience and Remote Sensing Symposium . Seoul, South Korea : IEEE: 2250 - 2253 [ DOI: 10.1109/IGARSS.2005.1525421 http://dx.doi.org/10.1109/IGARSS.2005.1525421 ]

Lv Z Y , Zhang P L , Benediktsson J A and Shi W Z . 2014 . Morphological profiles based on differently shaped structuring elements for classification of images with very high spatial resolution . IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 7 ( 12 ): 4644 - 4652 [ DOI: 10.1109/jstars.2014.2328618 http://dx.doi.org/10.1109/jstars.2014.2328618 ]

Maggiori E , Tarabalka Y , Charpiat G and Alliez P . 2017 . Can semantic labeling methods generalize to any city? The inria aerial image labeling benchmark // Proceedings of 2017 IEEE International Geoscience and Remote Sensing Symposium . Fort Worth, Texas, USA : IEEE: 3226 - 3229 [ DOI: 10.1109/IGARSS.2017.8127684 http://dx.doi.org/10.1109/IGARSS.2017.8127684 ]

Marmanis D , Schindler K , Wegner J D , Galliani S , Datcu M and Stilla U . 2018 . Classification with an edge: improving semantic image segmentation with boundary detection . ISPRS Journal of Photogrammetry and Remote Sensing , 135 : 158 - 172 [ DOI: 10.1016/j.isprsjprs.2017.11.009 http://dx.doi.org/10.1016/j.isprsjprs.2017.11.009 ]

Ming D P , Luo J C , Shen Z F , Wang M and Sheng H . 2005 . Research on information extraction and target recognition from high resolution remote sensing image . Science of Surveying and Mapping , 30 ( 3 ): 18 - 20

明冬萍 , 骆剑承 , 沈占锋 , 汪闽 , 盛昊 . 2005 . 高分辨率遥感影像信息提取与目标识别技术研究 . 测绘科学 , 30 ( 3 ): 18 - 20 [ DOI: 10.3771/j.issn.1009-2307.2005.03.004 http://dx.doi.org/10.3771/j.issn.1009-2307.2005.03.004 ]

Ronneberger O , Fischer P and Brox T . 2015 . U-Net: convolutional networks for biomedical image segmentation // Proceedings of 2015 International Conference on Medical Image Computing and Computer-Assisted Intervention . Munich, Germany : Springer: 234 - 241 [ DOI: 10.1007/978-3-319-24574-4_28 http://dx.doi.org/10.1007/978-3-319-24574-4_28 ]

Tan Q L . 2010 . Urban building extraction from VHR multi-spectral images using object-based classification . Acta Geodaetica et Cartographica Sinica , 39 ( 6 ): 618 - 623

谭衢霖 . 2010 . 高分辨率多光谱影像城区建筑物提取研究 . 测绘学报 , 39 ( 6 ): 618 - 623

Tian S H , Zhang X F , Tian J and Sun Q . 2016 . Random forest classification of wetland landcovers from multi-sensor data in the arid region of Xinjiang, China . Remote Sensing , 8 ( 11 ): 954 [ DOI: 10.3390/rs8110954 http://dx.doi.org/10.3390/rs8110954 ]

Van Der Walt S , Schönberger J L , Nunez-Iglesias J , Boulogne F , Warner J D , Yager N , Gouillart E , Yu T and the Scikit-Image Contributors . 2014 . Scikit-image: image processing in Python . PeerJ , 2 : e 453 [ DOI: 10.7717/peerj.453 http://dx.doi.org/10.7717/peerj.453 ]

Wu H , Cheng Z P , Shi W Z , Miao Z L and Xu C C . 2014 . An object-based image analysis for building seismic vulnerability assessment using high-resolution remote sensing imagery . Natural Hazards , 71 ( 1 ): 151 - 174 [ DOI: 10.1007/s11069-013-0905-6 http://dx.doi.org/10.1007/s11069-013-0905-6 ]

Wu G , Guo Z , Shi X , Chen Q , Xu Y , Shibasaki R , Shao X . 2018 . A boundary regulated network for accurate roof segmentation and outline extraction . Remote Sensing ， 10 ( 8 ), 1195 [ DOI: 10.3390/rs10081195 http://dx.doi.org/10.3390/rs10081195 ]

文章被引用时，请邮件提醒。

提交

点云深度学习基准数据集

融合LT-1升降轨地表形变观测的泸定9·5地震灾后滑坡易发性评价

结合Sentinel-1时序数据与MultiRocket-RF模型的小微湿地提取研究

基于多源时序遥感产品的非洲森林损毁成因识别与分析

基于多尺度监督对比学习的高光谱图像分类网络