IEEE Geoscience and Remote Sensing Letters· 2018Q1
Road Extraction by Deep Residual U-Net
- 3,079citations
- Q1SCImago
- 2018year
Short summary
A novel deep neural network, combining residual learning with U-Net architecture, significantly improves road extraction from aerial images.
AI-generated from the title and abstract; the full text is not read.
Key points
- Introduces a Deep Residual U-Net for semantic segmentation of aerial images.
- Residual units facilitate training of deeper networks.
- Rich skip connections enable fewer parameters with improved performance.
- Outperforms existing state-of-the-art methods on a public road dataset.
AI-generated from the title and abstract; the full text is not read.
Abstract
Road extraction from aerial images has been a hot research topic in the field of remote sensing image analysis. In this letter, a semantic segmentation neural network, which combines the strengths of residual learning and U-Net, is proposed for road area extraction. The network is built with residual units and has similar architecture to that of U-Net. The benefits of this model are twofold: first, residual units ease training of deep networks. Second, the rich skip connections within the network could facilitate information propagation, allowing us to design networks with fewer parameters, however, better performance. We test our network on a public road data set and compare it with U-Net and other two state-of-the-art deep-learning-based road extraction methods. The proposed approach outperforms all the comparing methods, which demonstrates its superiority over recently developed state of the arts.
The authors' abstract, as published at the source. IEEE Geoscience and Remote Sensing Letters, 2018 · DOI ↗
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