CoupleUNet: Swin Transformer coupling CNNs makes strong contextual encoders for VHR image road extraction
Author:
Affiliation:
1. School of Information Science, Beijing University of Chemical Technology, Beijing, Chaoyang, China
2. Key Laboratory of Image Interpretation and Intelligent Processing, Beijing University of Chemical Technology, Beijing, China
Funder
National Natural Science Foundation of China
Publisher
Informa UK Limited
Subject
General Earth and Planetary Sciences
Link
https://www.tandfonline.com/doi/pdf/10.1080/01431161.2023.2255353
Reference39 articles.
1. Improved Road Connectivity by Joint Learning of Orientation and Segmentation
2. Swin Transformers Make Strong Contextual Encoders for VHR Image Road Extraction
3. DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
4. Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
5. RADANet: Road Augmented Deformable Attention Network for Road Extraction From Complex High-Resolution Remote-Sensing Images
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