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Remote sensing image semantic segmentation method combining cosine annealing with atrous convolution

  • role： First author第一作者
  • Affiliation：

    School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China

  • Email：u201715748@hust.edu.cn
  • Introduction：唐振超，研究方向为高分辨率卫星遥感影像智能解译。E-mail： u201715748@hust.edu.cn
TANG Zhenchao1,  
  • Affiliation：

    Yellow River Survey, Planning, Design and Research Institute Co., Ltd, Zhengzhou 450003, China

WEI Wei2,  
  • Affiliation：

    School of Water Conservancy and Environment, Zhengzhou University, Zhengzhou 450001, China

LUO Weiran3,  
  • Affiliation：

    Yellow River Survey, Planning, Design and Research Institute Co., Ltd, Zhengzhou 450003, China

HU Jie2,  
  • role： Corresponding author通信作者
  • Affiliation：

    School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China

  • Email：zhangdongying@hust.edu.cn
  • Introduction：张东映，研究方向为卫星遥感智能解译与定量分析。E-mail：zhangdongying@hust.edu.cn
ZHANG Dongying1*

résumé

This study aims to capture the rich context information and multiscale feature information in remote sensing images, improve the integrated model strategy, and enhance the accuracy of semantic segmentation. Thus, this study proposes a high-resolution remote sensing image semantic segmentation method using cosine annealing with increasing period and multiscale atrous convolution.The multiscale parallel atrous convolution helps the network capture context information in a larger range and improves the ability of the network to recognize multiscale objects without increasing parameters. The method in this study uses the atrous convolution while discarding the pooling operation to maintain the spatial resolution. Meanwhile, the method adopts the fully connected conditional random field to add spatial and edge context information for making up for part of the position information missed by the atrous convolution. As a result, the outline of extraction objects by semantic segmentation fits the ground truth better. Moreover, the cosine annealing strategy with increasing period is introduced to adjust the learning rate and obtain a suitable number of local optimal solutions. We integrate the local optimal solutions in the method to further improve the pixel classification ability of the network.The overall accuracy and kappa coefficient of the proposed model, which are 86.6% and 81.8%, respectively, are better than those of the current advanced semantic segmentation models.The experimental results performed on the Gaofen image dataset show that the fusion of image context information and multiscale feature information can effectively identify objects with complex structures. Moreover, the model coupled with the period-increasing cosine annealing strategy could obtain better semantic segmentation accuracy than and less inference time than that coupled with the equal-period cosine annealing strategy.

mots-clés

high-resolution remote sensing image;semantic segmentation;cosine annealing with increasing period;multi-scale parallel atrous convolution;target extraction;in-context learning;conditional random field;multi-scale learning

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