融合网格注意力阀门和特征金字塔结构的高分辨率遥感影像建筑物提取

doi:10.12082/dqxxkx.2022.210571

地球信息科学学报 ›› 2022, Vol. 24 ›› Issue (9): 1785-1802.doi: 10.12082/dqxxkx.2022.210571

融合网格注意力阀门和特征金字塔结构的高分辨率遥感影像建筑物提取

于明洋¹(), 陈肖娴¹, 张文焯¹, 刘耀辉¹^,²^,³^,^*()

1.山东建筑大学测绘地理信息学院,济南 250101
2.河北省地震动力学重点实验室,三河 065201
3.山东科技大学测绘与空间信息学院,青岛 266590

收稿日期:2021-09-06 修回日期:2021-10-16 出版日期:2022-09-25 发布日期:2022-11-25
通讯作者: *刘耀辉（1991— ）,男,山东海阳人,博士,讲师,主要从事遥感大数据与模式识别、灾害管理等研究。 E-mail: liuyaohui20@sdjzu.edu.cn
作者简介:于明洋（1978— ）,男,山东东阿人,硕士,副教授,主要从事地理信息工程研发、深度学习和大数据分析研究。 E-mail: ymy@sdjzu.edu.cn
基金资助:
国家自然科学基金项目(41801308);河北省地震动力学重点实验室开放基金项目(FZ212203);山东省自然科学基金项目(ZR2021QD074);国家对地观测科学数据中心开放基金项目(NODAOP2020008)

Building Extraction on High-Resolution Remote Sensing Images Using Attention Gates and Feature Pyramid Structure

YU Mingyang¹(), CHEN Xiaoxian¹, ZHANG Wenzhuo¹, LIU Yaohui¹^,²^,³^,^*()

1. School of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan 250101, China
2. Hebei Key Laboratory of Earthquake Dynamics, Sanhe 065201, China
3. College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China

Received:2021-09-06 Revised:2021-10-16 Online:2022-09-25 Published:2022-11-25
Contact: LIU Yaohui
Supported by:
National Natural Science Foundation of China(41801308);Hebei Key Laboratory of Earthquake Dynamics,(FZ212203);Natural Science Foundation of Shandong Province(ZR2021QD074);Open Research Fund of National Earth Observation Data Center(NODAOP2020008)

摘要/Abstract

摘要：

在高分辨率遥感影像中提取建筑物轮廓是地区基础建设信息统计的一项重要任务。适应性较强的深度学习方法已在建筑物提取研究中取得较大进展,受网络模型对影像特征表达的局限性,存在局部建筑轮廓边缘模糊的问题。本研究提出一种基于注意力的U型特征金字塔网络（AFP-Net）可以聚焦高分遥感影像中不同形态的建筑物结构,实现建筑物轮廓的高效提取。AFP-Net模型通过基于网格的注意力阀门Attention Gates模块抑制输入影像中的无关区域,凸出影像中建筑物的显性特征;通过特征金字塔注意力Feature Pyramid Attention模块增加高维特征图的感受野,减少采样中的细节损失。基于WHU建筑物数据集训练优化AFP-Net模型,测试结果表明AFP-Net模型能够较清晰地识别出建筑物轮廓,在预测性能上有更好的目视效果,在测试结果的总体精度和交并比上较U-Net模型分别提高0.67%和1.34%。结果表明,AFP-Net模型实现了高分遥感影像中建筑物提取的结果精度及预测性能的有效提升。

关键词: 高分辨率遥感影像, 建筑物提取, 深度学习, WHU数据集, AFP-Net模型, 注意力阀门, 特征金字塔注意力

Abstract:

Building extraction from high-resolution remote sensing images is an important task of regional infrastructure information statistics. In recent years, due to the rapid development of aviation and aerospace science and technology, the data availability of fine resolution remote sensing images increases. The traditional methods such as manual visual interpretation or expert feature construction cannot balance the high efficiency and high precision for the results generation using high-resolution images. Nowadays, the adaptive deep learning method has gradually made great progress in the study of building extraction. Typically, the U-shaped U-Net network originated from the semantic segmentation model for medical images has been widely used. Its structure has good computational performance and segmentation accuracy and has been used as the basic structure of semantic segmentation for remote sensing images. However, the use of only the basic network model has limitations on the expression of image features, which could cause blurring of local building contour when extracting buildings from high-resolution remote sensing images. This paper proposes an Attention U Feature Pyramid Network (AFP-Net) that can focus on different forms of building structures in high-resolution remote sensing images to efficiently extract the details of buildings. The AFP-Net model suppresses the irrelevant areas in the input image through the grid-based Attention Gates (AGs) module and highlights the dominant features of buildings in the image. The Feature Pyramid Attention (FPA) module increases the receptive field of high dimensional feature map and reduces the loss of detail in sampling. In this paper, the AFP-Net model is trained and optimized based on WHU building dataset. The test results show that, compared with U-Net, the accuracy and Intersection over Union of the proposed method are improved by 0.67 % and 1.34 %, respectively using the test data of WHU dataset. In addition, this paper compares the detailed features of different models for convex and concave parts of the building contour, and the AFP-Net model can clearly identify the edge of the building. The results demonstrate that the proposed method can effectively improve the prediction accuracy of building detail extraction.

Key words: high-resolution remote sensing image, building extraction, deep learning, WHU dataset, AFP-Net model, attention gates, feature pyramid attention

于明洋, 陈肖娴, 张文焯, 刘耀辉. 融合网格注意力阀门和特征金字塔结构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(9): 1785-1802.DOI:10.12082/dqxxkx.2022.210571

YU Mingyang, CHEN Xiaoxian, ZHANG Wenzhuo, LIU Yaohui. Building Extraction on High-Resolution Remote Sensing Images Using Attention Gates and Feature Pyramid Structure[J]. Journal of Geo-information Science, 2022, 24(9): 1785-1802.DOI:10.12082/dqxxkx.2022.210571

图/表 14

图1

图2

表1

图3

图4

图5

图6

图7

表2

图8

表3

图9

表4

图10

参考文献 53

[1]	吴炜, 骆剑承, 沈占锋, 等. 光谱和形状特征相结合的高分辨率遥感图像的建筑物提取方法[J]. 武汉大学学报·信息科学版, 2012, 37(7):800-805.
	[ Wu W, Luo J C, Shen Z F, et al. Building extraction from high resolution remote sensing imagery based on spatial-spectral method[J]. Geomatics and Information Science of Wuhan University, 2012, 37(7):800-805. ] DOI: 10.13203/j.whugis2012.07.022 doi: 10.13203/j.whugis2012.07.022
[2]	Lunetta R S, Johnson D M, Lyon J G, et al. Impacts of imagery temporal frequency on land-cover change detection monitoring[J]. Remote Sensing of Environment, 2004, 89(4):444-454. DOI: 10.1016/j.rse.2003.10.022 doi: 10.1016/j.rse.2003.10.022
[3]	杜培军, 夏俊士, 薛朝辉, 等. 高光谱遥感影像分类研究进展[J]. 遥感学报, 2016, 20(2):236-256.
	[ Du P J, Xia J S, Xue Z H, et al. Review of hyperspectral remote sensing image classification[J]. Journal of Remote Sensing, 2016, 20(2):236-256. ] DOI: 10.11834/jrs.20165022 doi: 10.11834/jrs.20165022
[4]	Liu Y H, Zhou J, Qi W H, et al. ARC-net: An efficient network for building extraction from high-resolution aerial images[J]. IEEE Access, 2020, 8:154997-155010. DOI: 10.1109/ACCESS.2020.3015701 doi: 10.1109/ACCESS.2020.3015701
[5]	Lin C G, Nevatia R. Building detection and description from a single intensity image[J]. Computer Vision and Image Understanding, 1998, 72(2):101-121. DOI: 10.1006/cviu.1998.0724 doi: 10.1006/cviu.1998.0724
[6]	黄金库, 冯险峰, 徐秀莉, 等. 基于知识规则构建和形态学修复的建筑物提取研究[J]. 地理与地理信息科学, 2011, 27(4):28-31.
	[ Huang J K, Feng X F, Xu X L, et al. A study of building extraction based on morphological rehabilitation and rule-oriented classification[J]. Geography and Geo-Information Science, 2011, 27(4):28-31. ]
[7]	方鑫, 陈善雄. 密集城区高分辨率遥感影像建筑物提取[J]. 测绘通报, 2019(4):79-83.
	[ Fang X, Chen S X. High resolution remote sensing image building extraction in dense urban areas[J]. Bulletin of Surveying and Mapping, 2019(4):79-83. ] DOI: 10.13474/j.cnki.11-2246.2019.0117 doi: 10.13474/j.cnki.11-2246.2019.0117
[8]	He K M, Zhang X Y, Ren S Q, et al. Deep residual learning for image recognition[C]// 2016 IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 2016:770-778. DOI: 10.1109/CVPR.2016.90 doi: 10.1109/CVPR.2016.90
[9]	Huang G, Liu Z, van der Maaten L, et al. Densely connected convolutional networks[C]// 2017 IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 2017:2261-2269. DOI: 10.1109/CVPR.2017.243 doi: 10.1109/CVPR.2017.243
[10]	Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks[J]. Communications of the ACM, 2017, 60(6):84-90. DOI: 10.1145/3065386 doi: 10.1145/3065386
[11]	Szegedy C, Ioffe S, Vanhoucke V, et al. Inception-v4, inception-ResNet and the impact of residual connections on learning[C]// AAAI'17:Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence. 2017:4278-4284.
[12]	Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition[EB/OL]. 2014: arXiv: 1409.1556. https://arxiv.org/abs/1409.1556
[13]	Shelhamer E, Long J, Darrell T. Fully convolutional networks for semantic segmentation[C]// IEEE Transactions on Pattern Analysis and Machine Intelligence. IEEE,: 640-651. DOI: 10.1109/TPAMI.2016.2572683 doi: 10.1109/TPAMI.2016.2572683
[14]	Badrinarayanan V, Kendall A, Cipolla R. SegNet: A deep convolutional encoder-decoder architecture for image segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(12):2481-2495. DOI: 10.1109/TPAMI.2016.2644615 doi: 10.1109/TPAMI.2016.2644615 pmid: 28060704
[15]	Ronneberger O, Fischer P, Brox T. U-net: Convolutional networks for biomedical image segmentation[M]// Lecture Notes in Computer Science. Cham: Springer International Publishing, 2015:234-241. DOI: 10.1007/978-3-319-24574-4_28 doi: 10.1007/978-3-319-24574-4_28
[16]	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 and Machine Intelligence, 2018, 40(4):834-848. DOI: 10.1109/TPAMI.2017.2699184 doi: 10.1109/TPAMI.2017.2699184
[17]	Lv X W, Ming D P, Chen Y Y, et al. Very high resolution remote sensing image classification with SEEDS-CNN and scale effect analysis for superpixel CNN classification[J]. International Journal of Remote Sensing, 2019, 40(2):506-531. DOI: 10.1080/01431161.2018.1513666 doi: 10.1080/01431161.2018.1513666
[18]	Chen Y Y, Ming D P, Lv X W. Superpixel based land cover classification of VHR satellite image combining multi-scale CNN and scale parameter estimation[J]. Earth Science Informatics, 2019, 12(3):341-363. DOI: 10.1007/s12145-019-00383-2 doi: 10.1007/s12145-019-00383-2
[19]	Zhang C X, Yue P, Tapete D, et al. A multi-level context-guided classification method with object-based convolutional neural network for land cover classification using very high resolution remote sensing images[J]. International Journal of Applied Earth Observation and Geoinformation, 2020, 88:102086. DOI: 10.1016/j.jag.2020.102086 doi: 10.1016/j.jag.2020.102086
[20]	Chen S X, Shi W Z, Zhou M T, et al. Automatic building extraction via adaptive iterative segmentation with LiDAR data and high spatial resolution imagery fusion[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020, 13:2081-2095. DOI: 10.1109/JSTARS.2020.2992298 doi: 10.1109/JSTARS.2020.2992298
[21]	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, 2020, 24(9):1134-1142. ]
[22]	Guo H N, Shi Q, Du B, et al. Scene-driven multitask parallel attention network for building extraction in high-resolution remote sensing images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 59(5):4287-4306. DOI: 10.1109/TGRS.2020.3014312 doi: 10.1109/TGRS.2020.3014312
[23]	朱盼盼, 李帅朋, 张立强, 等. 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. doi: 10.12082/dqxxkx.2021.190805
	[ Zhu P P, Li S P, Zhang L Q, et al. Multitask learning-based building extraction from high-resolution remote sensing images[J]. Journal of Geo-information Science, 2021, 23(3):514-523. ] DOI: 10.12082/dqxxkx.2021.190805 doi: 10.12082/dqxxkx.2021.190805
[24]	Alshehhi R, Marpu P R, Woon W L, et al. Simultaneous extraction of roads and buildings in remote sensing imagery with convolutional neural networks[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2017, 130:139-149. DOI: 10.1016/j.isprsjprs.2017.05.002 doi: 10.1016/j.isprsjprs.2017.05.002
[25]	谢跃辉, 李百寿, 刘聪娜. 结合多种影像特征与CNN的城市建筑物提取[J]. 遥感信息, 2020, 35(5):80-88.
	[ Xie Y H, Li B S, Liu C N. Urban building extraction by combining multiple image features and CNN[J]. Remote Sensing Information, 2020, 35(5):80-88. ] DOI: 10.3969/j.issn.1000-3177.2020.05.010 doi: 10.3969/j.issn.1000-3177.2020.05.010
[26]	刘亦凡, 张秋昭, 王光辉, 等. 利用深度残差网络的遥感影像建筑物提取[J]. 遥感信息, 2020, 35(2):59-64.
	[ Liu Y F, Zhang Q Z, Wang G H, et al. Building extraction in remote sensing imagery based on deep residual network[J]. Remote Sensing Information, 2020, 35(2):59-64. ] DOI: 10.3969/j.issn.1000-3177.2020.02.010 doi: 10.3969/j.issn.1000-3177.2020.02.010
[27]	Chen M, Wu J J, Liu L Z, et al. DR-net: An improved network for building extraction from high resolution remote sensing image[J]. Remote Sensing, 2021, 13(2):294. DOI: 10.3390/rs13020294 doi: 10.3390/rs13020294
[28]	Guo M Q, Liu H, Xu Y 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
[29]	Zhang L L, Wu J S, Fan Y, et al. An efficient building extraction method from high spatial resolution remote sensing images based on improved mask R-CNN[J]. Sensors (Basel, Switzerland), 2020, 20(5):1465. DOI: 10.3390/s20051465 doi: 10.3390/s20051465
[30]	Yang H, Wu P H, Yao X D, et al. Building extraction in very high resolution imagery by dense-attention networks[J]. Remote Sensing, 2018, 10(11):1768. DOI: 10.3390/rs10111768 doi: 10.3390/rs10111768
[31]	Ye Z R, Fu Y Y, Gan M Y, et al. Building extraction from very high resolution aerial imagery using joint attention deep neural network[J]. Remote Sensing, 2019, 11(24):2970. DOI: 10.3390/rs11242970 doi: 10.3390/rs11242970
[32]	Jin Y W, Xu W B, Zhang C, et al. Boundary-aware refined network for automatic building extraction in very high-resolution urban aerial images[J]. Remote Sensing, 2021, 13(4):692. DOI: 10.3390/rs13040692 doi: 10.3390/rs13040692
[33]	Shelhamer E, Long J, Darrell T. Fully convolutional networks for semantic segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(4):640-651. DOI: 10.1109/TPAMI.2016.2572683 doi: 10.1109/TPAMI.2016.2572683 pmid: 27244717
[34]	Zhao H S, Shi J P, Qi X J, et al. Pyramid scene parsing network[C]// 2017. IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 2017:6230-6239. DOI: 10.1109/CVPR.2017.660 doi: 10.1109/CVPR.2017.660
[35]	Fu J, Liu J, Tian H J, et al. Dual attention network for scene segmentation[C]// 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2019:3141-3149. DOI: 10.1109/CVPR.2019.00326 doi: 10.1109/CVPR.2019.00326
[36]	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. ]
[37]	Vaswani A, Shazeer N, Parmar N, et al. Attention is all You need[EB/OL]. 2017: arXiv: 1706.03762. https://arxiv.org/abs/1706.03762
[38]	Li H C, Xiong P F, An J, et al. Pyramid attention network for semantic segmentation[EB/OL]. 2018: arXiv: 1805. 10180. https://arxiv.org/abs/1805.10180
[39]	Zhao W, Ivanov I, Persello C, et al. Building outline delineation: From very high resolution remote sensing imagery to polygons with an improved end-to-end learning framework[J]. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2020,: 731-735. DOI: 10.5194/isprs-archives-xliii-b2-2020-731-2020 doi: 10.5194/isprs-archives-xliii-b2-2020-731-2020
[40]	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):574-586. DOI: 10.1109/TGRS.2018.2858817 doi: 10.1109/TGRS.2018.2858817
[41]	Oktay O, Schlemper J, Folgoc L L, et al. Attention U-net: Learning where to look for the pancreas[EB/OL]. 2018: arXiv: 1804.03999. https://arxiv.org/abs/1804.03999
[42]	Sandler M, Howard A, Zhu M L, et al. MobileNetV2:inverted residuals and linear bottlenecks[C]// 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. IEEE, 2018:4510-4520. DOI: 10.1109/CVPR.2018.00474 doi: 10.1109/CVPR.2018.00474
[43]	Han S, Pool J, Tran J, et al. Learning both weights and connections for efficient neural networks[EB/OL]. 2015: arXiv: 1506.02626. https://arxiv.org/abs/1506.02626
[44]	Woo S, Park J, Lee J Y, et al. CBAM: convolutional block attention module[M]//Computer Vision-ECCV 2018. Cham: Springer International Publishing, 2018:3-19. DOI: 10.1007/978-3-030-01234-2_1 doi: 10.1007/978-3-030-01234-2_1
[45]	Hu J, Shen L, Albanie S, et al. Squeeze-and-excitation networks[C]// IEEE Transactions on Pattern Analysis and Machine Intelligence. IEEE,: 2011- 2023. DOI: 10.1109/TPAMI.2019.2913372 doi: 10.1109/TPAMI.2019.2913372
[46]	Guo M H, Xu T X, Liu J J, et al. Attention mechanisms in computer vision: A survey[EB/OL]. 2021: arXiv: 2111.07624. https://arxiv.org/abs/2111.07624
[47]	Shen Y, Fang Z J, Gao Y B, et al. Coronary arteries segmentation based on 3D FCN with attention gate and level set function[J]. IEEE Access, 2019, 7:42826-42835. DOI: 10.1109/ACCESS.2019.2908039 doi: 10.1109/ACCESS.2019.2908039
[48]	刘耀辉. 面向地震风险评估的高分辨率遥感影像建筑物信息提取与研究[D]. 北京: 中国地震局地质研究所, 2020.
	[ Liu Y H. Extraction and research on building information from high-resolution remote sensing image for seismic risk assessment[D]. Beijing: Institute of Geology, China Earthquake Administration, 2020. ] DOI: 10.27489/d.cnki.gzdds.2020.000001 doi: 10.27489/d.cnki.gzdds.2020.000001
[49]	Mnih V. Machine Learning for Aerial Image Labeling[D]. Toronto: University of Toronto, Graduate Department of Computer Science, 2013.
[50]	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
[51]	Xing H Q, Zhu L Y, Feng Y Y, et al. An adaptive change threshold selection method based on land cover posterior probability and spatial neighborhood information[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021, 14:11608-11621. DOI: 10.1109/JSTARS.2021.3124491 doi: 10.1109/JSTARS.2021.3124491
[52]	Dixit M, Chaurasia K, Mishra V K. Automatic building extraction from high-resolution satellite images using deep learning techniques[C]// Proceedings of the International Conference on Paradigms of Computing, Communication and Data Sciences. Singapore: Springer Singapore, 2021:773-783. DOI: 10.1007/978-981-15-7533-4_61 doi: 10.1007/978-981-15-7533-4_61
[53]	Xing H Q, Zhu L Y, Hou D Y, et al. Integrating change magnitude maps of spectrally enhanced multi-features for land cover change detection[J]. International Journal of Remote Sensing, 2021, 42(11):4284-4308. DOI: 10.1080/01431161.2021.1892860 doi: 10.1080/01431161.2021.1892860

结构块	类别	核尺寸	输出通道数	输出尺寸/pixel
Block 1-4	Conv1	(3, 3)	64	256×256
	Maxpool1	(2, 2)	64	128×128
	Conv2	(3, 3)	128	128×128
	Maxpool2	(2, 2)	128	64×64
	Conv3	(3, 3)	256	64×64
	Maxpool3	(2, 2)	256	32×32
	Conv4	(3, 3)	512	32×32
	Maxpool4	(2, 2)	512	16×16
Block 5	PPM		1024	16×16
Block 6-9	Up_conv4	Up-(3, 3) Conv-(2, 2)	512	32×32
	AG4		512	32×32
	Up4		512	32×32
	Up_conv3	Up-(3, 3) Conv-(2, 2)	256	64×64
	AG3		256	64×64
	Up3		256	64×64
	Up_conv2	Up-(3, 3) Conv-(2, 2)	128	128×128
	AG2		512	128×128
	Up2		512	128×128
	Up_conv1	Up-(3, 3) Conv-(2, 2)	64	256×256
	AG 1		64	256×256
	Up1		64	256×256
Block 10	Conv_1x1	(1, 1)	1	256×256
Block 10	Sigmoid		1	256×256

模型	总体精度	精密度	交并比
FCN8s	0.9574	0.9439	0.7857
SegNet	0.9487	0.9102	0.7761
DANet	0.9207	0.9555	0.7519
PSPNet	0.9661	0.9491	0.8681
U-Net	0.9601	0.9582	0.8568
AFP-Net	0.9668	0.9490	0.8702

模型	参数量/M	计算量/(G Mac)	训练时间(s/epoch)
SegNet	16.31	23.77	222
FCN8s	134.27	62.81	393
DANet	49.48	10.93	335
PSPNet	31.2	11.03	225
U-Net	13.4	23.77	212
AFP-Net	27.95	48.76	295

模型	总体精度/%	精密度/%	交并比/%	参数量/M	计算量/(G Mac)	训练时间/(s/epoch)
U型基础架构	0.9410	0.9745	0.8336	34.53	50.65	312
AFP-Net (AGs)	0.9701	0.9616	0.9034	34.88	51.76	316
AFP-Net (FPA)	0.9613	0.9862	0.8811	27.6	47.65	287
AFP-Net (AGs + FPA)	0.9731	0.9723	0.9112	27.95	48.76	295

融合网格注意力阀门和特征金字塔结构的高分辨率遥感影像建筑物提取

Building Extraction on High-Resolution Remote Sensing Images Using Attention Gates and Feature Pyramid Structure

RichHTML

PDF (PC)

赞

可视化

摘要/Abstract

引用本文

使用本文

图/表 14

参考文献 53

相关文章 15

Metrics

本文评价

推荐阅读 0

[1]	左溪冰, 刘智, 金飞, 林雨准, 王淑香, 刘潇, 李美霖. 面向高光谱影像小样本分类的全局-局部特征自适应融合方法[J]. 地球信息科学学报, 2023, 25(8): 1699-1716.
[2]	林娜, 何静, 王斌, 唐菲菲, 周俊宇, 郭江. 结合植被光谱特征与Sep-UNet的城市植被信息智能提取方法[J]. 地球信息科学学报, 2023, 25(8): 1717-1729.
[3]	张春菊, 刘文聪, 张雪英, 叶鹏, 汪陈, 朱少楠, 张达玉. 基于本体的金矿知识图谱构建方法[J]. 地球信息科学学报, 2023, 25(7): 1269-1281.
[4]	张彤, 刘仁宇, 王培晓, 高楚林, 刘杰, 王望舒. 感知物理先验的机器学习及其在地理空间智能中的研究前景[J]. 地球信息科学学报, 2023, 25(7): 1297-1311.
[5]	曹兴文, 吴孟泉, 郑雪婷, 郑宏伟, 李映祥, 张安安. 室内空间布局约束下的在线跟踪注册学习方法[J]. 地球信息科学学报, 2023, 25(7): 1418-1431.
[6]	赵丹, 杜萍, 刘涛, 令振飞. 融合图自编码器与GRU的城市盗窃犯罪时空分布预测模型[J]. 地球信息科学学报, 2023, 25(7): 1448-1463.
[7]	金广垠, 沙恒宇, 张金雷, 黄金才. 基于路段-路口联合建模的对偶图卷积网络的行程时间估计方法[J]. 地球信息科学学报, 2023, 25(7): 1500-1513.
[8]	刘潇, 刘智, 林雨准, 王淑香, 左溪冰. 面向遥感影像场景分类的类中心知识蒸馏方法[J]. 地球信息科学学报, 2023, 25(5): 1050-1063.
[9]	侯慧太, 蓝朝桢, 徐青. 基于卫星影像全局和局部深度学习特征检索的无人机绝对定位方法[J]. 地球信息科学学报, 2023, 25(5): 1064-1074.
[10]	张金雷, 陈奕洁, Panchamy Krishnakumari, 金广垠, 王骋程, 杨立兴. 基于注意力机制的城市轨道交通网络级多步短时客流时空综合预测模型[J]. 地球信息科学学报, 2023, 25(4): 698-713.
[11]	衡雪彪, 许捍卫, 唐璐, 汤恒, 许怡蕾. 基于改进全卷积神经网络模型的土地覆盖分类方法研究[J]. 地球信息科学学报, 2023, 25(3): 495-509.
[12]	于方圆, 曹家玮, 李发源, 李思进. 顾及对象特征的地面式光伏电站提取及减碳效益评估[J]. 地球信息科学学报, 2023, 25(3): 529-545.
[13]	高建文, 管海燕, 彭代锋, 许正森, 康健, 季雅婷, 翟若雪. 基于局部-全局语义特征增强的遥感影像变化检测网络模型[J]. 地球信息科学学报, 2023, 25(3): 625-637.
[14]	江宝得, 许少芬, 王进, 王淼. 基于条件生成对抗网络的电子地图可视水印去除方法[J]. 地球信息科学学报, 2023, 25(2): 288-297.
[15]	谢谦, 陆明, 谢春山. 基于LBS和深度学习的旅游景区客流量的高时频预测[J]. 地球信息科学学报, 2023, 25(2): 298-310.