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Journal of Shanghai Jiaotong University(Science) >

2026 , Vol. 31 >Issue 2: 348 - 358

DOI: https://doi.org/10.1007/s12204-024-2721-4

Automation & Computer Technologies

High Resolution Remote Sensing Image Segmentation Method with Improved DeepLabv3+

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  • 1. School of Automation and Information Engineering, Sichuan University of Science and Engineering, Yibin 643002, Sichuan, China; 2. Artificial Intelligence Key Laboratory of Sichuan Province, Sichuan University of Science and Engineering, Yibin 643002, Sichuan, China; 3. Chengdu Tianxun Microsatellite Technology Co., Ltd., Chengdu 610200, China

Received date: 2023-06-29

  Accepted date: 2023-08-27

  Online published: 2024-04-22

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Abstract

In order to address the challenges associated with poor semantic segmentation results of classical semantic segmentation networks in high-resolution remote sensing images, limited performance in complex scenes, a large number of network parameters, and high training costs, this study proposes an efficient segmentation method for high-resolution remote sensing images based on an improved DeepLabv3+ approach. The method focuses on three key aspects: reducing the number of network parameters, minimizing computation volume, and enhancing performance. First, the proposed method replaces the original DeepLabv3+ backbone network Xception, which is computationally heavy, with the lighter MobileNetV2 network for feature extraction. This substitution helps reduce the number of network parameters while maintaining effective feature extraction. Second, a lightweight convolutional block attention module (CBAM) is added after the feature extraction module to enhance the network’s feature extraction capability. The inclusion of CBAM further reduces the number of network parameters. Last, coordinate attention is introduced after the shallow features obtained from the feature extraction module. This addition allows the network to focus more on relevant features in the image, while disregarding irrelevant background information. Experimental results demonstrate the effectiveness of the proposed method. In the segmentation task of the high-resolution image dataset, the method achieves a mean intersection over union (mIoU) of 75.33%. This result surpasses mainstream semantic segmentation networks such as SegNet, PSPNet, and U-Net by 12.49%, 3.16%, and 1.62% respectively. Furthermore, the proposed model has a relatively low number of network parameters, with only 6.02 × 106 parameters, and a computation volume of 26.45 GFLOPs. This balance between computational efficiency and segmentation accuracy makes the model highly valuable for edge computing applications.

Cite this article

Tao Hongjie, Li Zhaofei, Qi Fei, Chen Jingjue, Zhou Hao . High Resolution Remote Sensing Image Segmentation Method with Improved DeepLabv3+[J]. Journal of Shanghai Jiaotong University(Science), 2026 , 31(2) : 348 -358 . DOI: 10.1007/s12204-024-2721-4

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