An information extraction model of roads from high-resolution remote sensing images based on improved Deeplabv3+

ZHAO Linghu; YUAN Xiping; GAN Shu; HU Lin; QIU Mingyu

doi:10.6046/zrzyyg.2021460

2023 Vol. 35, No. 1

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Article navigation > Remote Sensing for Natural Resources > 2023 > 35(1): 107-114

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ZHAO Linghu, YUAN Xiping, GAN Shu, HU Lin, QIU Mingyu. 2023. An information extraction model of roads from high-resolution remote sensing images based on improved Deeplabv3+. Remote Sensing for Natural Resources, 35(1): 107-114. doi: 10.6046/zrzyyg.2021460

Citation:

ZHAO Linghu, YUAN Xiping, GAN Shu, HU Lin, QIU Mingyu. 2023. An information extraction model of roads from high-resolution remote sensing images based on improved Deeplabv3+. Remote Sensing for Natural Resources, 35(1): 107-114. doi: 10.6046/zrzyyg.2021460

An information extraction model of roads from high-resolution remote sensing images based on improved Deeplabv3+

1. School of Land and Resources Engineering, Kunming University of Science and Technology, Kunming 650093, China；
2. Surveying and Mapping Geo-Informatics Technology Research Center on Plateau Mountains of Yunnan Higher Education, Kunming 650093, China；
3. School of Earth Science and Engineering, West Yunnan University of Applied Sciences, Dali 671000, China

Received Date: 27 December 2021

Revised Date: 15 March 2023

Publish Date: 20 March 2023

Abstract

Abstract

Aiming at the problems of poor extraction effect and slow extraction speed of traditional road extraction methods in the information extraction of roads from high-resolution remote sensing images, this study proposed a new information extraction model based on improved Deeplabv3+. In the new model, the combination of the MobileNetv2 backbone feature extraction network with the Dice Loss function effectively balanced the contradiction between the precision and speed of road information extraction from high-resolution remote sensing images. As a result, high extraction precision was achieved while meeting timeliness requirements by reducing model parameters. The experimental results based on the open-source road information extraction dataset show that: ① The road information extraction model proposed in this study was feasible for high-resolution remote sensing images, with overall accuracy of up to 98.71%; ② In terms of the information extraction speed, the new model had an average frame number of 120.05 and parameter amount of only 5.81 M. Therefore, the new model was more lightweight lighter than original models, meeting the timeliness requirements. Therefore, the model proposed in this study meets the timeliness requirements by greatly reducing the parameter amount while ensuring high extraction accuracy. This study provides a new philosophy and method for improving the accuracy and speed of road information extraction from high-resolution images.
- remote sensing image /
- road information extraction /
- deep learning /
- semantic segmentation /
- Deeplabv3+ model

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References

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