Based on the improved depth residual Unet high-resolution remote sensing road extraction method

Ya Wen

doi:10.1117/12.3019577

23 January 2024 Based on the improved depth residual Unet high-resolution remote sensing road extraction method

Ya Wen

Proceedings Volume 12978, Fourth International Conference on Geology, Mapping, and Remote Sensing (ICGMRS 2023); 129780E (2024) https://doi.org/10.1117/12.3019577
Event: 2023 4th International Conference on Geology, Mapping and Remote Sensing (ICGMRS 2023), 2023, wuhan, China

Abstract

Road information is an important part of remote sensing information. The accurate and efficient extraction of road information from high-resolution remote sensing images is closely related to human life. Deep residual Unet has certain achievements in road extraction, but there is still room for improvement in the integrity of road extraction results. In order to improve the segmentation completeness, robustness, and anti-occlusion ability of the model, this paper proposes an improved dual decoder structure based on the deep residual Unet (ResUnet) to solve the problem of incomplete road extraction in the deep residual Unet. High-resolution remote sensing image road information extraction method. In the original model, the bottom up-sampling structure was first added, which retained features to facilitate segmentation and expand the receptive field. Then add spatial channel compression and activation modules to correct the input features and enhance meaningful features. Experiments on the Massachusetts road data set show that the improved method has improved evaluation indicators compared with the original network. Precision, Recall, F1-score and Dice coefficients have reached 91.84% and 87.27%, respectively. 89.55%, 88.89%, and has certain advantages in anti-interference and extraction integrity.

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

Citation Download Citation

Ya Wen "Based on the improved depth residual Unet high-resolution remote sensing road extraction method", Proc. SPIE 12978, Fourth International Conference on Geology, Mapping, and Remote Sensing (ICGMRS 2023), 129780E (23 January 2024); https://doi.org/10.1117/12.3019577

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KEYWORDS

Roads

Remote sensing

Image segmentation

Convolution

RGB color model

Education and training

Deep learning

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