地球信息科学学报 ›› 2022, Vol. 24 ›› Issue (6): 1189-1203.doi: 10.12082/dqxxkx.2022.210727
收稿日期:
2021-11-15
修回日期:
2021-12-01
出版日期:
2022-06-25
发布日期:
2022-08-25
通讯作者:
*郑南山(1974— ),男,安徽安庆人,博士,教授,主要从事遥感数据处理与应用。E-mail: znshcumt@163.com作者简介:
张 华(1979— ),男,安徽合肥人,博士,副教授,主要从事遥感数据智能解译及GIS理论与应用研究。E-mail: zhhua_79@163.com
基金资助:
ZHANG Hua1(), ZHENG Xiangcheng1, ZHENG Nanshan1,*(
), SHI Wenzhong2
Received:
2021-11-15
Revised:
2021-12-01
Online:
2022-06-25
Published:
2022-08-25
Supported by:
摘要:
从高空间分辨率图像(HSRI)中提取建筑物信息在遥感应用领域具有重要意义。然而,由于遥感影像中的建筑物尺度变化大、背景复杂和外观变化大等因素,从HSRI中自动提取建筑物仍然是一项具有挑战性的任务。特别是从影像中同时提取小型建筑物群和具有精确边界的大型建筑物时,难度更大。为解决这些问题,本文提出了一种端到端的编码器-解码器神经网络模型,用于从HSRI中自动提取建筑物。所设计的网络称为MAEU-CNN(Multiscale Feature Enhanced U-shaped CNN with Attention Block and Edge Constraint)。首先,在设计的网络编码部分加入多尺度特征融合(MFF)模块,使网络能够更好地聚集多个尺度特征。然后,在编码器和解码器部分之间添加了多尺度特征增强模块(MFEF),以获得不同尺寸的感受野,用于获取更多的多尺度上下文信息。在跳跃连接部分引入双重注意机制,自适应地选择具有代表性的特征图用于提取建筑物。最后,为了进一步解决MAEU-CNN中由于池化及卷积操作导致的分割结果边界模糊的问题,引入多任务学习机制,将建筑物的边界几何信息融入网络中以优化提取的建筑物边界,最终获得精确边界的建筑物信息。MAEU-CNN在ISPRS Vaihingen语义标记数据集和WHU航空影像数据集2种不同尺度建筑物数据集上进行了试验分析,在ISPRS Vaihingen语义标记数据集上,MAEU-CNN在精度、F1分数和IoU指标中获得了最高精度,分别达到了93.4%、93.62%和88.01%;在WHU航空影像数据集上,召回率、F1分数和IoU指标中也获得了最高精度,分别达到了95.45%、95.58%和91.54%。结果表明,本文所提出的MAEU-CNN从遥感图像中提取建筑物信息精度较高,并且对于不同尺度具有较强的鲁棒性。
张华, 郑祥成, 郑南山, 史文中. 基于MAEU-CNN的高分辨率遥感影像建筑物提取[J]. 地球信息科学学报, 2022, 24(6): 1189-1203.DOI:10.12082/dqxxkx.2022.210727
ZHANG Hua, ZHENG Xiangcheng, ZHENG Nanshan, SHI Wenzhong. Building Extraction from High Spatial Resolution Imagery based on MAEU-CNN[J]. Journal of Geo-information Science, 2022, 24(6): 1189-1203.DOI:10.12082/dqxxkx.2022.210727
[1] |
Wang J, Yang X C, Qin X B, et al. An efficient approach for automatic rectangular building extraction from very high-resolution optical satellite imagery[J]. IEEE Geoscience and Remote Sensing Letters, 2015, 12(3):487-491. DOI: 10.1109/LGRS.2014.2347332
doi: 10.1109/LGRS.2014.2347332 |
[2] |
Sirmacek B, Unsalan C. Urban-area and building detection using SIFT key points and graph theory[J]. IEEE Transactions on Geoscience and Remote Sensing, 2009, 47(4):1156-1167. DOI: 10.1109/TGRS.2008.2008440
doi: 10.1109/TGRS.2008.2008440 |
[3] |
Huang X, Zhang L P. Morphological building/shadow index for building extraction from high-resolution imagery over urban areas[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2012, 5(1):161-172. DOI: 10.1109/JSTARS.2011.2168195
doi: 10.1109/JSTARS.2011.2168195 |
[4] |
Li E, Xu S B, Meng W L, et al. Building extraction from remotely sensed images by integrating saliency cue[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017, 10(3):906-919. DOI: 10.1109/JSTARS.2016.2603184
doi: 10.1109/JSTARS.2016.2603184 |
[5] |
Du S H, Zhang F L, Zhang X Y. Semantic classification of urban buildings combining VHR image and GIS data: An improved random forest approach[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2015, 105:107-119. DOI: 10.1016/j.isprsjprs.2015.03.011
doi: 10.1016/j.isprsjprs.2015.03.011 |
[6] |
Shi Y L, Li Q Y, Zhu X X. Building segmentation through a gated graph convolutional neural network with deep structured feature embedding[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2020, 159:184-197. DOI: 10.1016/j.isprsjprs.2019.11.004
doi: 10.1016/j.isprsjprs.2019.11.004 |
[7] |
Zhu X X, Tuia D, Mou L C, et al. Deep learning in remote sensing: A comprehensive review and list of resources[J]. IEEE Geoscience and Remote Sensing Magazine, 2017, 5(4):8-36. DOI: 10.1109/MGRS.2017.2762307
doi: 10.1109/MGRS.2017.2762307 |
[8] |
Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation[C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, 1:3431-3440. DOI: 10.1109/CVPR.2015.7298965
doi: 10.1109/CVPR.2015.7298965 |
[9] |
Ji S P, Wei S Q, Lu M. Fully convolutional networks for multisource building extraction from an open aerial and satellite imagery data set[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(1):574-596. DOI: 10.1109/TGRS.2018.2858817
doi: 10.1109/TGRS.2018.2858817 |
[10] |
Yi Y N, Zhang Z J, Zhang W C, et al. Semantic segmentation of urban buildings from VHR remote sensing imagery using a deep convolutional neural network[J]. Remote Sensing, 2019, 10(15):1774-1792. DOI: 10.3390/rs11151774
doi: 10.3390/rs11151774 |
[11] |
Xia L G, Zhang X B, Zhang J X, et al. Building extraction from very-high-resolution remote sensing images using semi-supervised semantic edge detection[J]. Remote Sensing, 2021, 13(11):2187-2206. DOI: 10.3390/rs13112187
doi: 10.3390/rs13112187 |
[12] |
Liu Y Y, Chen D Y, Ma A L, et al. Multiscale U-shaped CNN building instance extraction framework with edge constraint for high-spatial-resolution remote sensing imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 59(7):6106-6120. DOI: 10.1109/TGRS.2020.3022410
doi: 10.1109/TGRS.2020.3022410 |
[13] |
Ronneberger O, Fischer P, Brox T. U-Net: Convolutional networks for biomedical image segmentation[C]// International Conference on Medical image computing and computer assisted intervention. Springer, Cham, 2015:234-241. DOI: 10.1007/978-3-319-24574-4_28
doi: 10.1007/978-3-319-24574-4_28 |
[14] |
Liu J Y, Wang S S, Hou X W, et al. A deep residual learning serial segmentation network for extracting buildings from remote sensing imagery[J]. International Journal of Remote Sensing, 2020, 41(14):5573-5587. DOI: 10.1080/01431161.2020.1734251
doi: 10.1080/01431161.2020.1734251 |
[15] |
Diakogiannis F I, Waldner F, Caccetta P, et al. ResUNet-a: A deep learning framework for semantic segmentation of remotely sensed data[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2020, 162:94-114. DOI: 10.1016/j.isprsjprs.2020.01.013
doi: 10.1016/j.isprsjprs.2020.01.013 |
[16] |
Zhang X Q, Xiao Z H, Li D Y, et al. Semantic segmentation of remote sensing images using multiscale decoding network[J]. IEEE Geoscience and Remote Sensing Letters, 2019, 16(9):1492-1496. DOI: 10.1109/LGRS.2019.2901592
doi: 10.1109/LGRS.2019.2901592 |
[17] |
徐佳伟, 刘伟, 单浩宇,等. 基于PRCUnet的高分遥感影像建筑物提取[J]. 地球信息科学学报, 2021, 23(10):1838-1849.
doi: 10.12082/dqxxkx.2021.210283 |
[ Xu J W, Liu W, Shan H Y, et al. High-resolution remote sensing image building extraction based on PRCUnet[J]. Journal of Geo-information Science, 2021, 23(10):1838-1849. ] DOI: 10.12082/dqxxkx.2021.210283
doi: 10.12082/dqxxkx.2021.210283 |
|
[18] | 张玉鑫, 颜青松, 邓非. 高分辨率遥感影像建筑物提取多路径RSU网络法[J]. 测绘学报, 2021, 50(10):1-10. |
[ Zhang Y X, Yan Q S, Deng F. Multi-path RSUnetwork method for high-resolution remote sensing image building extraction[J]. Acta Geodaetica Sinica, 2021, 50(10):1-10. ] DOI: 10.11947/j.AGCS.2021.20200508
doi: 10.11947/j.AGCS.2021.20200508 |
|
[19] |
Rastogi K, Bodani B, Sharma S A. Automatic building footprint extraction from very high-resolution imagery using deep learning techniques[J]. Geocarto International [online]. DOI: 10.1080/10106049.2020.1778100
doi: 10.1080/10106049.2020.1778100 |
[20] |
唐璎, 刘正军, 杨懿,等. 基于特征增强和ELU的神经网络建筑物提取研究[J]. 地球信息科学学报, 2021, 23(4):692-709.
doi: 10.12082/dqxxkx.2021.200130 |
[ Tang Y, Liu Z J, Yang Y, et al. Research on building extraction based on neural network with feature enhancement and ELU activation function[J]. Journal of Geo-information Science, 2021, 23(4):692-709. ] DOI: 1 0.12082/dqxxkx.2021.200130
doi: 1 0.12082/dqxxkx.2021.200130 |
|
[21] |
Liu Y H, Gross L, Li Z Q, et al. Automatic building extraction on high-resolution remote sensing imagery using deep convolutional encoder-decoder with spatial pyramid pooling[J]. IEEE Access, 2019, 7:128774-128786. DOI: 10.1109/ACCESS.2019.2940527
doi: 10.1109/ACCESS.2019.2940527 |
[22] |
Wei S Q, Ji S P, Lu M. Toward automatic building footprint delineation from aerial images using CNN and regularization[J]. IEEE Transactions on Geoscience and Remote Sensing, 2020, 58(3):2178-2189. DOI: 10.1109/TG RS.2019.2954461
doi: 10.1109/TG RS.2019.2954461 |
[23] |
Shrestha S, Vanneschi L. Improved fully convolutional network with conditional random fields for building extraction[J]. Remote Sensing, 2018, 10(7):1135-1155. DOI: 10.3390/rs10071135
doi: 10.3390/rs10071135 |
[24] |
Yuan J Y. Learning building extraction in aerial scenes with convolutional networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, 40(11):2793-2798. DOI: 10.1109/TPAMI.2017.2750680
doi: 10.1109/TPAMI.2017.2750680 |
[25] |
Pan S M, Tao Y L, Nie C C, et al. PEGNet: Progressive edge guidance network for semantic segmentation of remote sensing images[J]. IEEE Geoscience and Remote Sensing Letters, 2021, 18(4):637-641. DOI: 10.1109/LGRS.2020.2983464
doi: 10.1109/LGRS.2020.2983464 |
[26] |
Xia L G, Zhang J X, Zhang X B, et al. Precise extraction of buildings from high-resolution remote sensing images based on semantic edges and segmentation[J]. Remote Sensing, 2021, 13(16):3083-3104. DOI: 10.3390/rs13163083
doi: 10.3390/rs13163083 |
[27] |
Sun Y, Zhang X C, Zhao X Y, et al. Extracting building boundaries from high resolution optical images and LiDAR data by integrating the convolutional neural network and the active contour model[J]. Remote Sensing, 2018, 10(9):1459. DOI: 10.3390/rs10091459
doi: 10.3390/rs10091459 |
[28] | Vaihingen 2D Semantic Labeling-ISPRS. [Online]. Available:http://www2.isprs.org/commissions/comm3/wg4/semantic-labeling.html . |
[1] | 陈振, 陈芸芝, 吴婷, 李佳优. 面向高分遥感影像道路提取的轻量级双注意力和特征补偿残差网络模型[J]. 地球信息科学学报, 2022, 24(5): 949-961. |
[2] | 蒯宇, 王彪, 吴艳兰, 陈搏涛, 陈兴迪, 薛维宝. 基于多尺度特征感知网络的城市植被无人机遥感分类[J]. 地球信息科学学报, 2022, 24(5): 962-980. |
[3] | 吴新辉, 毛政元, 翁谦, 施文灶. 利用基于残差多注意力和ACON激活函数的神经网络提取建筑物[J]. 地球信息科学学报, 2022, 24(4): 792-801. |
[4] | 杨先增, 周亚男, 张新, 李睿, 杨丹. 融合边缘特征与语义信息的人工坑塘精准提取方法[J]. 地球信息科学学报, 2022, 24(4): 766-779. |
[5] | 龙怡灿, 雷蓉, 董杨, 李东子, 赵琛琛. 基于YOLOv5算法的飞机类型光学遥感识别[J]. 地球信息科学学报, 2022, 24(3): 572-582. |
[6] | 周欣昕, 吴艳兰, 李梦雅, 郑智腾. 基于特征分离机制的深度学习植被自动提取方法[J]. 地球信息科学学报, 2021, 23(9): 1675-1689. |
[7] | 李国清, 柏永青, 杨轩, 陈正超, 余海坤. 基于深度学习的高分辨率遥感影像土地覆盖自动分类方法[J]. 地球信息科学学报, 2021, 23(9): 1690-1704. |
[8] | 叶凡, 孙玉, 陈崇成, 于大宇. 基于地理标记照片的个性化景点推荐方法[J]. 地球信息科学学报, 2021, 23(8): 1391-1400. |
[9] | 刘戈, 姜小光, 唐伯惠. 特征优选与卷积神经网络在农作物精细分类中的应用研究[J]. 地球信息科学学报, 2021, 23(6): 1071-1081. |
[10] | 许泽宇, 沈占锋, 李杨, 柯映明, 李硕, 王浩宇, 焦淑慧. 结合模糊度和形态学指数约束的深度学习建筑物提取[J]. 地球信息科学学报, 2021, 23(5): 918-927. |
[11] | 唐璎, 刘正军, 杨懿, 顾海燕, 杨树文. 基于特征增强和ELU的神经网络建筑物提取研究[J]. 地球信息科学学报, 2021, 23(4): 692-709. |
[12] | 朱盼盼, 李帅朋, 张立强, 李洋. 基于多任务学习的高分辨率遥感影像建筑提取[J]. 地球信息科学学报, 2021, 23(3): 514-523. |
[13] | 郭紫甜, 王春梅, 刘欣, 庞国伟, 朱梦阳, 王晋卿. 基于小流域抽样单元的中国FROM-GLC30数据精度评价[J]. 地球信息科学学报, 2021, 23(3): 524-535. |
[14] | 李传林, 黄风华, 胡威, 曾江超. 基于Res_AttentionUnet的高分辨率遥感影像建筑物提取方法[J]. 地球信息科学学报, 2021, 23(12): 2232-2243. |
[15] | 冯叶涵, 陈亮, 贺晓冬. 基于百度街景的SVF计算及其在城市热岛研究中的应用[J]. 地球信息科学学报, 2021, 23(11): 1998-2012. |
|