RPE-Net: Road Patch Extraction Network for Improving the Integrity of Road Extraction Results from Remote Sensing Images

Deep learning (DP) based road extraction methods often produce fragmented results. Direct optimization of end-to-end DP-based methods requires the design of more complex network structures to enhance the model's adaptability in complex scenarios. To tackle this challenge, this paper adopts a no...

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Bibliographic Details
Published inIGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium pp. 7497 - 7500
Main Authors Wang, Yuchuan, Zhu, Chenhui, Tong, Ling, Yang, Jiaxing, Xiao, Fanghong, Dong, Xiaohuan, Wen, Jiang
Format Conference Proceeding
LanguageEnglish
Published IEEE 07.07.2024
Subjects
Deep learning
Design methodology
Manuals
Multi-directional information
Post Processing
Remote Sensing
Road Extraction
Roads
Semantic segmentation
Sensors
Training
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Summary:Deep learning (DP) based road extraction methods often produce fragmented results. Direct optimization of end-to-end DP-based methods requires the design of more complex network structures to enhance the model's adaptability in complex scenarios. To tackle this challenge, this paper adopts a novel approach that treats the discrepancy (the road breakage part) between road prediction and ground-truth as the extraction target, thereby constructing a highly efficient and lightweight semantic segmentation network, termed the Road Patch Extraction Network (RPE-Net). RPE-Net includes multi-directional striped residual (MDSR) encoder, multi-directional striped pooling (MDSP) units, and multi-directional striped decoder (MDSD). The structure is similar to LinkNet34, but the overall size of the network parameters is just 1.56MB, which is 1/50 of LinkNet34. The post-processing datasets used for training can be semi-automatically generated by the algorithm, and only a small amount of manual intervention can be used for training. A large number of experiments showt hat the post-processing method proposed in this paper has extremely high speed and generalization ability.
ISSN:2153-7003
DOI:10.1109/IGARSS53475.2024.10641978