Strip Attention Networks for Road Extraction

In recent years, deep learning methods have been widely used for road extraction in remote sensing images. However, the existing deep learning semantic segmentation networks generally show poor continuity in road segmentation due to the high-class similarity between roads and buildings surrounding r...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2022-09, Vol.14 (18), p.4516
Main Authors: Huan, Hai, Sheng, Yu, Zhang, Yi, Liu, Yuan
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
attention module
Cities
Continuity
Datasets
Deep learning
encoder-decoder structure
Image segmentation
Information processing
Modules
Networks
Neural networks
Occlusion
Remote sensing
remote sensing image
road extraction
Roads
Roads & highways
Semantics
Strip
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Summary:In recent years, deep learning methods have been widely used for road extraction in remote sensing images. However, the existing deep learning semantic segmentation networks generally show poor continuity in road segmentation due to the high-class similarity between roads and buildings surrounding roads in remote sensing images, and the existence of shadows and occlusion. To deal with this problem, this paper proposes strip attention networks (SANet) for extracting roads in remote sensing images. Firstly, a strip attention module (SAM) is designed to extract the contextual information and spatial position information of the roads. Secondly, a channel attention fusion module (CAF) is designed to fuse low-level features and high-level features. The network is trained and tested using the CITY-OSM dataset, DeepGlobe road extraction dataset, and CHN6-CUG dataset. The test results indicate that SANet exhibits excellent road segmentation performance and can better solve the problem of poor road segmentation continuity compared with other networks.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs14184516