1. Road Extraction Using a Dual Attention Dilated-LinkNet Based on Satellite Images and Floating Vehicle Trajectory Data
- Author
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Zhiyong Lv, Jingyu Wang, Qixin Wang, Lipeng Gao, Honghai Qiao, Hongping Gan, Jiangbin Zheng, and Wenzhong Shi
- Subjects
Hyperparameter ,Atmospheric Science ,road extraction ,Computer science ,business.industry ,QC801-809 ,Deep learning ,Feature extraction ,Geophysics. Cosmic physics ,Image segmentation ,floating vehicle trajectory ,Ocean engineering ,Data structure alignment ,satellite image ,Redundancy (engineering) ,Trajectory ,Computer vision ,Artificial intelligence ,Computers in Earth Sciences ,business ,TC1501-1800 ,Dual attention ,Network model - Abstract
Automatic extraction of road from multisource remote sensing data has always been a challenging task. Factors such as shadow occlusion and multisource data alignment errors prevent current deep learning-based road extraction methods from acquiring road features with high complementarity, redundancy, and crossover. Unlike previous works that capture contexts by multiscale feature fusion, we propose a dual attention dilated-LinkNet (DAD-LinkNet) to adaptively integrate local road features with their global dependencies by joint using satellite image and floating vehicle trajectory data. First, a joint least-squares feature matching-based floating vehicle trajectory correction model is used to correct the floating vehicle trajectory; then a convolutional network model DAD-LinkNet based on a dual-attention mechanism is proposed, and road features are extracted from the channel domain and spatial domain of the target image in turn by constructing a dual-attention module in the dilated convolutional layer and adopting a cascade connection; a weighted hyperparameter loss function is used as the loss function of the model; finally, the road extraction is completed based on the proposed DAD-LinkNet model. Experiments on three datasets show that the proposed DAD-LinkNet model outperforms the state-of-the-art methods in terms of accuracy and connectivity.
- Published
- 2021