Mixture-of-experts for semantic segmentation of remoting sensing image

Shaofeng He; Qiu Cheng; Yu Huai; Zhongke Zhu; Jie Ding

doi:10.1117/12.3035091

19 July 2024 Mixture-of-experts for semantic segmentation of remoting sensing image

Shaofeng He, Qiu Cheng, Yu Huai, Zhongke Zhu, Jie Ding

Proceedings Volume 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024); 132131Z (2024) https://doi.org/10.1117/12.3035091
Event: International Conference on Image Processing and Artificial Intelligence (ICIPAl2024), 2024, Suzhou, China

Abstract

To address the issues of complex backgrounds, imbalanced samples, and inconsistent sample scales in remote sensing image segmentation, this study proposes a remote sensing image segmentation network based on Swin Transformer (SW). The backbone network is designed based on Swin Transformer and incorporates a Mixture of Experts (MoE) structure to separate the model's parameter space. Different scales and scenes of remote sensing images activate different expert models for inference, addressing the complex backgrounds and imbalanced sample issues. By adding a channel attention module to the decoder to aggregate spatial and channel information weights of remote sensing images, the multiscale information of buildings in remote sensing images is effectively utilized, thus reducing the loss of image details during training. Experiments conducted on the public remote sensing semantic segmentation dataset NAIC validate the effectiveness of the proposed algorithm, which achieves mean Intersection over Union (mIoU) and F1 scores of 84.06 and 90.12, respectively, outperforming DeeplabV3+ by 3.44 and 3.77.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Shaofeng He, Qiu Cheng, Yu Huai, Zhongke Zhu, and Jie Ding "Mixture-of-experts for semantic segmentation of remoting sensing image", Proc. SPIE 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024), 132131Z (19 July 2024); https://doi.org/10.1117/12.3035091

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