GCM+-LANet：Global Convolution Module+ and Local Attention Network for Semantic Segmentation of Remote Sensing Images

doi:10.11873/j.issn.1004-0323.2022.4.0820

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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Cite this article:

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

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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