地球信息科学学报 ›› 2022, Vol. 24 ›› Issue (4): 792-801.doi: 10.12082/dqxxkx.2022.210530
吴新辉1,2(), 毛政元1,2,*(
), 翁谦3,4, 施文灶5,6
收稿日期:
2021-09-02
修回日期:
2021-10-10
出版日期:
2022-04-25
发布日期:
2022-06-25
通讯作者:
*毛政元(1964— ),男,湖南邵阳人,博士,教授,博士生导师,主要从事时空系统认知与测度、高分影像信息提取与地表变化检测、地理空间数据不确定性分析及其应用、土地资源信息化管理与决策服务研究。 E-mail: zymao@fzu.edu.cn作者简介:
吴新辉(1995— ),男,福建莆田人,硕士生,主要从事深度学习、遥感影像的分析与应用研究。E-mail: wdlan4869@163.com
基金资助:
WU Xinhui1,2(), MAO Zhengyuan1,2,*(
), WENG Qian3,4, SHI Wenzao5,6
Received:
2021-09-02
Revised:
2021-10-10
Online:
2022-04-25
Published:
2022-06-25
Contact:
MAO Zhengyuan
Supported by:
摘要:
针对目前主流深度学习网络模型应用于高空间分辩率遥感影像建筑物提取存在的内部空洞、不连续以及边缘缺失与边界不规则等问题,本文在U-Net模型结构的基础上通过设计新的激活函数(ACON)、集成残差以及通道-空间与十字注意力模块,提出RMAU-Net模型。该模型中的ACON激活函数允许每个神经元自适应地激活或不激活,有利于提高模型的泛化能力和传输性能;残差模块用于拓宽网络深度并降低训练和学习的难度,获取深层次语义特征信息;通道-空间注意力模块用于增强编码段与解码段信息的关联、抑制无关背景区域的影响,提高模型的灵敏度;十字注意力模块聚合交叉路径上所有像素的上下文信息,通过循环操作捕获全局上下文信息,提高像素间的全局相关性。以Massachusetts数据集为样本的建筑物提取实验表明,在所有参与比对的7个模型中,本文提出的RMAU-Net模型交并比与F1分数2项指标最优、查准率和查全率两项指标接近最优, RMA-UNet总体效果优于同类模型。通过逐步添加每个模块来进一步验证各模块的有效性以及本文所提方法的可靠性。
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.DOI:10.12082/dqxxkx.2022.210530
WU Xinhui, MAO Zhengyuan, WENG Qian, SHI Wenzao. A Neural Network based on Residual Multi-attention and ACON Activation Function for Extract Buildings[J]. Journal of Geo-information Science, 2022, 24(4): 792-801.DOI:10.12082/dqxxkx.2022.210530
[1] |
施文灶, 毛政元. 基于图割与阴影邻接关系的高分辨率遥感影像建筑物提取方法[J]. 电子学报, 2016,44(12):2849-2854.
doi: 10.3969/j.issn.0372-2112.2016.12.006 |
[ Shi W Z, Mao Z Y. Building extraction from high resolution remotely sensed imagery based on shadows and graph-cut segmentation[J]. Acta Electronica Sinica, 2016,44(12):2849-2854. ] DOI: 10.3969/j.issn.0372-2112.2016.12.006
doi: 10.3969/j.issn.0372-2112.2016.12.006 |
|
[2] |
Kim T, Muller J. Development of a graph-based approach for building detection[J]. Image and Vision Computing, 1999,17(1):3-14. DOI: 10.1016/S0262-8856(98)00092-4
doi: 10.1016/S0262-8856(98)00092-4 |
[3] |
Jung C R, Schramm R. Rectangle detection based on a windowed Hough transform[C]//17th Brazilian Symposium on Computer Graphics and Image Processing. IEEE, 2004:113-120. DOI: 10.1109/SIBGRA.2004.1352951
doi: 10.1109/SIBGRA.2004.1352951 |
[4] |
Huang X, Zhang L. A multidirectional and multiscale morphological index for automatic building extraction from multispectral GeoEye-1 imagery[J]. Photogrammetric Engineering & Remote Sensing, 2011,77(7):721-732. DOI: 10.14358/PERS.77.7.721
doi: 10.14358/PERS.77.7.721 |
[5] | 冯凡, 王双亭, 张津, 等. 基于尺度自适应全卷积网络的遥感影像建筑物提取[J]. 激光与光电子学进展, 2021:1-20. |
[ Feng F, Wang S T, Zhang J, et al. Building extraction from remote sensing imagery based on scale-adaptive fully convolutional network[J]. Laser & Optoelectronics Progress, 2021:1-20. ] | |
[6] |
Shao Z, Tang P, Wang Z, et al. BRRNet: A fully convolutional neural network for automatic building extraction from high-resolution remote sensing images[J]. Remote Sensing, 2020,12(6):1050. DOI: 10.3390/rs12061050
doi: 10.3390/rs12061050 |
[7] |
Zhao W, Persello C, Stein A. Building outline delineation: From aerial images to polygons with an improved end-to-end learning framework[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2021,175:119-131. DOI: 10.1016/j.isprsjprs.2021.02.014
doi: 10.1016/j.isprsjprs.2021.02.014 |
[8] | 崔卫红, 熊宝玉, 张丽瑶. 多尺度全卷积神经网络建筑物提取[J]. 测绘学报, 2019,48(5):597-608. |
[ Cui W H, Xiong B Y, Zhang L Y. Multi-scale fully convolutional neural network for building extraction[J]. Acta Geodaetica et Cartographica Sinica, 2019,48(5):597-608. ] DOI: CNKI:SUN:CHXB.0.2019-05-008
doi: CNKI:SUN:CHXB.0.2019-05-008 |
|
[9] |
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 |
[10] |
Jha D, Smedsrud P H, Riegler M A, et al. Resunet++: An advanced architecture for medical image segmentation[C]//2019 IEEE International Symposium on Multimedia (ISM). IEEE, 2019:225-2255. DOI: 10.1109/ISM46123.2019.00049
doi: 10.1109/ISM46123.2019.00049 |
[11] | Oktay O, Schlemper J, Folgoc L L, et al. Attention u-net: Learning where to look for the pancreas[J]. arXiv preprint arXiv:1804.03999, 2018. |
[12] |
Pan X, Yang F, Gao L, et al. Building extraction from high-resolution aerial imagery using a generative adversarial network with spatial and channel attention mechanisms[J]. Remote Sensing, 2019,11(8):917. DOI: doi.org/10.3390/rs11080917
doi: doi.org/10.3390/rs11080917 |
[13] | 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. |
[ Ji S P, Wei S Q. Building extraction via convolution neural networks from an open remote sensing building dataset[J]. Acta Geodaetica et Cartographica Sinica, 2019,48(4):448-459. ] DOI: 10.11947/j.AGCS.2019.20180206.
doi: 10.11947/j.AGCS.2019.20180206 |
|
[14] |
Guo M, Liu H, Xu Y, et al. Building extraction based on U-Net with an attention block and multiple losses[J]. Remote Sensing, 2020,12(9):1400. DOI: 10.3390/rs12091400
doi: 10.3390/rs12091400 |
[15] |
Huang Z L, Wang X G, Huang L C, et al. Ccnet: Criss-cross attention for semantic segmentation[C]//The IEEE/CVF International Conference on Computer Vision. 2019:603-612. DOI: 10.1109/tpami.2020.3007032
doi: 10.1109/tpami.2020.3007032 |
[16] |
Zhang Z, Wang Y. JointNet: A common neural network for road and building extraction[J]. Remote Sensing, 2019,11(6):696. DOI: 10.3390/rs11060696
doi: 10.3390/rs11060696 |
[17] |
Zhang Z, Liu Q, Wang Y. Road extraction by deep residual u-net[J]. IEEE Geoscience and Remote Sensing Letters, 2018,15(5):749-753. DOI: 10.1109/LGRS.2018.2802944
doi: 10.1109/LGRS.2018.2802944 |
[18] |
Alom M Z, Yakopcic C, Hasan M, et al. Recurrent residual U-Net for medical image segmentation[J]. Journal of Medical Imaging, 2019,6(1):14006. DOI: 10.1117/1.JMI.6.1.014006
doi: 10.1117/1.JMI.6.1.014006 |
[19] | 陈凯强, 高鑫, 闫梦龙, 等. 基于编解码网络的航空影像像素级建筑物提取[J]. 遥感学报, 2020,24(9):1134-1142. |
[ Chen K Q, Gao X, Yan M L, et al. Building extraction in pixel level from aerial imagery with a deep encoder-decoder network[J]. Journal of Remote Sensing (Chinese), 2020,24(9):1134-1142. ] DOI: CNKI:SUN:YGXB.0.2020-09-008
doi: CNKI:SUN:YGXB.0.2020-09-008 |
|
[20] | Ramachandran P, Zoph B, Le Q V. Searching for activation functions[J]. arXiv preprint arXiv:1710.05941, 2017. |
[21] |
He K M, Zhang X Y, Ren S Q, et al. Deep residual learning for image recognition[C]//The IEEE conference on computer vision and pattern recognition. 2016:770-778. DOI: 10.1109/CVPR.2016.90
doi: 10.1109/CVPR.2016.90 |
[22] |
Wang X L, Girshick R, Gupta A, et al. Non-local neural networks[C]//The IEEE conference on computer vision and pattern recognition. 2018:7794-7803. DOI: 10.1109/CVPR.2018.00813
doi: 10.1109/CVPR.2018.00813 |
[23] | Mnih V. Machine learning for aerial image labeling[M]. University of Toronto (Canada), 2013. |
[24] |
Wang F, Jiang M Q, Qian C, et al. Residual attention network for image classification[C]//The IEEE conference on computer vision and pattern recognition. 2017:3156-3164. DOI: 10.1109/CVPR.2017.683
doi: 10.1109/CVPR.2017.683 |
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