A road extraction method based on improved PSPNet

Xiang Jie; Rongshaung Fan

doi:10.1117/12.2682633

1 June 2023 A road extraction method based on improved PSPNet

Xiang Jie, Rongshaung Fan

Author Affiliations +

Proceedings Volume 12710, International Conference on Remote Sensing, Surveying, and Mapping (RSSM 2023); 127100G (2023) https://doi.org/10.1117/12.2682633
Event: International Conference on Remote Sensing, Surveying, and Mapping (RSSM 2023), 2023, Changsha, China

Abstract

Aiming at the problems of low accuracy and missing of some details in road extraction by existing road extraction methods, this paper proposes a road extraction method integrating improved PSPNet. This method mainly improves the original PSPNet model by embedding SGE attention mechanism in the convolutional neural network stage to enhance the extraction accuracy. In pyramid pooling, Hybrid Dilated Convolution is used to replace global mean pooling to enhance the attention to details. In order to verify the effectiveness and feasibility of the proposed method, the improved model in this paper was compared with other common road extraction models. The experimental results show that compared with other methods, the proposed model has higher accuracy and integrity for road segmentation, and has certain practical application value.

Citation Download Citation

Xiang Jie and Rongshaung Fan "A road extraction method based on improved PSPNet", Proc. SPIE 12710, International Conference on Remote Sensing, Surveying, and Mapping (RSSM 2023), 127100G (1 June 2023); https://doi.org/10.1117/12.2682633

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KEYWORDS

Roads

Feature extraction

Convolution

Remote sensing

Education and training

Image segmentation

Data modeling

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