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Journal of Projectiles, Rockets, Missiles and Guidance >

2023 , Vol. 43 >Issue 4: 60 - 67

DOI: https://doi.org/10.15892/j.cnki.djzdxb.2023.04.009

Target Segmentation in Remote Sensing Images Based on Dilated Inception Attention U-Net

  • LI Ping ,
  • LI Na ,
  • MENG Lingyuan
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  • School of Computer and Software Engineering, Zhengzhou SIAS University, Zhengzhou 451150, Henan, China

Received date: 2023-01-01

  Online published: 2025-02-07

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Abstract

Aiming at the problem of object segmentation caused by the characteristics of multiple types, small size, large-size difference, large field-view, and complex environment-background of multi-scale targets in remote sensing images (RSIs), an attention dilated multi-scale U-Net (ADMSU-Net) is constructed for multi-scale target segmentation in RSIs (TSRSI). It consists of contracting subnet, expanding subnet, dilated convolutional residual connection and spatial attention connection. In the model, dilated multi-scale Inception module is introduced into its contracting and expanding subnets to learn the multi-scale advanced features without increasing computational cost, and the spatial attention mechanism is added into skip connection to capture the spatial correlation between features and improve TSRSI performance. The experimental results on the RSI dataset EORSSD containing multi-scale targets show that this method is effective and feasible, and the segmentation accuracy is more than 93%.

Key words： target segmentation in remote sensing images (TSRSI); U-Net; spatial attention mechanism; attention dilated multi-scale U-Net (ADMSU-Net)

Cite this article

LI Ping , LI Na , MENG Lingyuan . Target Segmentation in Remote Sensing Images Based on Dilated Inception Attention U-Net[J]. Journal of Projectiles, Rockets, Missiles and Guidance, 2023 , 43(4) : 60 -67 . DOI: 10.15892/j.cnki.djzdxb.2023.04.009

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