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Remote Sensing Technology and Application  2022, Vol. 37 Issue (4): 820-828    DOI: 10.11873/j.issn.1004-0323.2022.4.0820
    
GCM+-LANet:Global Convolution Module+ and Local Attention Network for Semantic Segmentation of Remote Sensing Images
Mengqian Weng1(),Lei Hu1(),Yongmei Zhang2,Jie Ling1,Yunhong Li1
1.Jiangxi Normal University,School of Computer Information Engineering,Nanchang 330022,China
2.North China University of Technology,School of Information,Beijing 100144,China
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Abstract  

Because of the variety, variable size, uneven distribution and complex background of ground objects, the classical image semantic segmentation network is difficult to achieve ideal segmentation results in remote sensing images. Local Attention Network (LANet) has achieved good results in remote sensing image semantic segmentation, but the segmentation effect of large-size, slender and small ground objects are not very good. Therefore, based on LANet a semantic segmentation network is proposed to high resolution remote sensing image. Firstly, Global Convolution Module+ (GCM+) aimed at global feature extraction is designed to enlarge the receptive field by combining convolution, which can improve the segmentation performance of large-size objects. Secondly, the activation function Funnel ReLU (FReLU) proposed to computer vision is used to solve the problem of missing slender and small targets. The experimental results show that the mean intersection over union of the network of the Potsdam dataset reaches 75.83 %, and the pixel accuracy reaches 94.95 %, which is greatly improved than LANet.

Key words:  Remote sensing image      Semantic segmentation      Global convolution module      Local attention network      Activation function     
Received:  31 August 2021      Published:  28 September 2022
TP75  
Corresponding Authors:  Lei Hu     E-mail:  1059257750@qq.com;hulei@jxnu.edu.cn
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Mengqian Weng
Lei Hu
Yongmei Zhang
Jie Ling
Yunhong Li

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Mengqian Weng,Lei Hu,Yongmei Zhang,Jie Ling,Yunhong Li. GCM+-LANet:Global Convolution Module+ and Local Attention Network for Semantic Segmentation of Remote Sensing Images. Remote Sensing Technology and Application, 2022, 37(4): 820-828.

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http://www.rsta.ac.cn/EN/10.11873/j.issn.1004-0323.2022.4.0820     OR     http://www.rsta.ac.cn/EN/Y2022/V37/I4/820

Fig.1  Architecture of PAM
Fig.2  Architecture of AEM
Fig.3  Architecture of the proposed GCM+-LANet
Fig.4  Architecture of the proposed GCM+
Fig.5  “BottleNeck” building block for RestNet50
Fig.6  ReLU, PReLU and FReLU activation functions
Fig.7  The Potsdam dataset
网络模型PA/%F1/%MIoU/%
FCN92.4077.2170.09
U-Net91.8277.0569.98
SegNet91.5575.9268.62
LANet91.8377.2970.19
DeepLab V3+86.9968.1459.34
Coordinate Attention90.9475.4567.94
GCM+-LANet(k=3)94.4280.9975.27
Table 1  Segmentation accuracy of different methods of Potsdam dataset
Fig.8  Comparison of prediction results of different network models of Potsdam dataset
Fig.9  Analysis of the influence of k on MIoU and parameter number
1 Chao P, Zhang X, Gang Y, et al. Large kernel matters-improve semantic segmentation by global convolutional network[C]∥ Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu, USA: IEEE, 2017: 4353-4361.
2 Likas A, Vlassis N, Verbeek J. The Global K-means clustering algorithm[J]. Pattern Recognition, 2003, 36(2): 451-461.DOI: .
doi: 10.1016/S0031-3203(02)00060-2
3 Carson C, Belongie S, Greenspan H, et al. Blobworld: Image segmentation using expectation-maximization and its application to image querying[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(8):1026-1038. DOI: .
doi: 10.1109/TPAMI.2002.1023800
4 Li Shuang, Ding Shengyan, Qian Yuexiang. The decisiontree classification and its application research in land cover[J]. Remote Sensing Technology and Application, 2002, 17(1): 6-11.
4 李爽, 丁圣彦, 钱乐祥. 决策树分类法及其在土地覆盖分类中的应用[J].遥感技术与应用, 2002, 17(1): 6-11.
5 Song M, Civco D. Road extraction using SVM and image segmentation[J]. Photogrammetric Engineering & Remote Sensing,2004,70(12): 1365-1371. DOI: .
doi: 10.14358/P-ERS. 70.12.1365
6 Stéphane G, Olivier G. A simple, fast, and accurate algorithm to estimate large phylogenies by maximum likelihood[J]. Systematic Bology, 2003, 52(5): 696-704. DOI: .
doi: 10.1080/10635150390235520
7 Jordan M I, Mitchell T M. Machine learning: Trends, perspectives, and prospects[J]. Science, 2015, 349(6245): 255-260. DOI: .
doi: 10.1126/science.aaa8415
8 Gu Xiaotian, Gao Xiaohong, Ma Huijuan, et al. Comparison of machine learning methods for land use/land cover classification in the complicated terrain regions[J].Remote Sensing Te-chnology and Application, 2019, 34(1):59-69.
8 谷晓天, 高小红, 马慧娟,等. 复杂地形区土地利用/土地覆被分类机器学习方法比较研究[J]. 遥感技术与应用, 2019, 34(1): 59-69.
9 Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation[C]∥ Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR),Boston,MA,USA,IEEE,2015:3431-3440.
10 Zhang H, Dana K, Shi J, et al. Context encoding for semantic segmentation[C]∥ Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition( CVPR). Salt Lake City, UT, USA, IEEE, 2018: 7151-7160.
11 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. Munich, Germany: Springer,2015: 234-241.
12 Badrinarayanan V, Kendall A, Cipolla R. Segnet: A deep convolutional encoder-decoder srchitecture for image segmentation[J]. IEEETransactions on Pattern Analysisand Machine Intelligence,2017,39(12):2481-2495. DOI: .
doi: 10.1109/TPAMI.2016.2644615
13 Chen L C, Papandreou G, Kokkinos I, et al. Semantic image segmentation with deep convolutional nets and fully connected CRFs[EB/OL]. arXiv Preprint arXiv: , 2014.
14 Chen L C, Papandreou G, Kokkinos I, et al. Deep lab: 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: .
doi: 10.1109/TPAMI.2017.2699184
15 Chen L C, Papandreou G, Schroff F, et al. Rethinking atrous convolution for semantic image segmentation[EB/OL]. arXiv Preprint arXiv: , 2017.
16 Chen L C, Zhu Y, Papandreou G, et al. Encoder decoder with atrous separable convolution for semantic image segmentation[C]∥ Proceedings of the European Conference on Computer Vision(ECCV). Munich,Germany:IEEE,2018:801-818.
17 Yang M D, Tseng H H, Hsu Y C, et al. Semantic segmentation using deep learning with vegetation indicesfor rice lodging identification in multi date UAV visible Images[J]. Remote Sensing, 2020, 12(4): 633-652. DOI: .
doi: 10.3390/rs12040633
18 Abdollahi A, Pradhan B, Alamri A M. An ensemble architecture of deep convolutional segnet and U-Net networks for building semantic segmentation from high resolution aerial images[J].Geocarto International,2020(3):116. DOI: .
doi: 10.1080/ 10106049.2020.1856199
19 He H, Yang D, Wang S, et al. Road extraction by using atrous spatial pyramid pooling integrated encoder decoder network and structural similarity loss[J]. Remote Sensing, 2019, 11(9): 1015-1030. DOI: .
doi: 10.3390/rs11091015
20 Ding L, Tang H, Bruzzone L. LANet: Local attention embedding to improve the semantic segmentation of remote sensing images[J]. IEEE Transactions on Geoscience and Remote Sensing,2020,59(1):426-435. DOI: .
doi: 10.1109/TGRS. 2020. 2994150
21 Hou Q, Zhou D, Feng J. Coordinate attention for efficient mobile network design[C]∥ Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition(CVPR). 2021: 13713-13722.
22 Ma N, Zhang X, Sun J. Funnel activation for visual recognition[C]∥ Proceedings of the European Conference onComputer Vision(ECCV),Glasgow, UK: Springer, 2020: 351-368.
23 He K, Zhang X, Ren S, et al. Deep residual learning for image recognition[C]∥ Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition(CVPR). Las Vegas, NV, USA: IEEE, 2016: 770-778.
24 Hu J, Shen L, Sun G. Squeeze and excitation networks[C]∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Salt Lake City, UT: IEEE, 2018: 7132-7141.
25 Rottensteiner F, Sohn G, Gerke M, et al. Results of the ISPRS benchmark on urban object detection and 3D building reconstruction[J]. ISPRS Journal of Photogrammetry and Remote Sensing,2014,93:256-271. DOI: .
doi: 10.1016/j.isprsjprs. 2013.10.00
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