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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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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
Corresponding Authors:  Lei Hu     E-mail:;
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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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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
DeepLab V3+86.9968.1459.34
Coordinate Attention90.9475.4567.94
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
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