Abstract
Complete and accurate road network information is an important basis in the detection of EHV transmission lines, and regular updates of road distribution near transmission lines are necessary and meaningful. However, no relevant research has been found for this application area, and coupled with the fact that roads themselves are significantly challenging, extracting roads with good connectivity and integrity in remote sensing images remains a problem to be solved. Therefore, in this paper, we develop a new end-to-end road extraction network, Multiple Attention Networks (MANet). Specifically, by fusing convolutional and self-attentive approaches, we focus on global contextual features to obtain an effective feature map. In addition, the Strip Multi-scale Channel Attention (SMCA) module is specifically designed for the line features of roads, focusing on extracting row and column features, while the Edge-aware Module (EAM) is used to extract connected and complete roads, aided by edge information. Meanwhile, in order to enhance the practicality of the study, a Mengxi Transmission Line Road Dataset was constructed independently following the processing process of remote sensing images in industrial production. By conducting relevant quantitative and qualitative experiments on this dataset and the publicly available CHN6-CUG dataset, it is fully verified that the method in this paper is superior to other advanced methods and can still extract roads with strong connectivity in complex backgrounds, which has good potential and outstanding advantages in practical applications.
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Acknowledgments
The authors gratefully acknowledge the financial supports by the National Natural Science Foundation of China under Grant No. 62077032, as well as the Inner Mongolia Science and Technology Plan Project under project No. 2021GG0159.
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Ren, Y., Bai, X., Han, Y., Hu, X. (2023). MANet: An End-To-End Multiple Attention Network for Extracting Roads Around EHV Transmission Lines from High-Resolution Remote Sensing Images. In: Yang, X., et al. Advanced Data Mining and Applications. ADMA 2023. Lecture Notes in Computer Science(), vol 14176. Springer, Cham. https://doi.org/10.1007/978-3-031-46661-8_37
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