Author:
Zhang Jinglin,Li Yuxia,Si Yu,Peng Bo,Xiao Fanghong,Luo Shiyu,He Lei
Abstract
Low-grade roads have complex features such as geometry, reflection spectrum, and spatial topology in remotely sensing optical images due to the different materials of those roads and also because they are easily obscured by vegetation or buildings, which leads to the low accuracy of low-grade road extraction from remote sensing images. To address this problem, this paper proposes a novel deep learning network referred to as SDG-DenseNet as well as a fusion method of optical and Synthetic Aperture Radar (SAR) data on decision level to extract low-grade roads. On one hand, in order to enlarge the receptive field and ensemble multi-scale features in commonly used deep learning networks, we develop SDG-DenseNet in terms of three modules: stem block, D-Dense block, and GIRM module, in which the Stem block applies two consecutive small-sized convolution kernels instead of the large-sized convolution kernel, the D-Dense block applies three consecutive dilated convolutions after the initial Dense block, and Global Information Recovery Module (GIRM) combines the ideas of dilated convolution and attention mechanism. On the other hand, considering the penetrating capacity and oblique observation of SAR, which can obtain information from those low-grade roads obscured by vegetation or buildings in optical images, we integrate the extracted road result from SAR images into that from optical images at decision level to enhance the extraction accuracy. The experimental result shows that the proposed SDG-DenseNet attains higher IoU and F1 scores than other network models applied to low-grade road extraction from optical images. Furthermore, it verifies that the decision-level fusion of road binary maps from SAR and optical images can further significantly improve the F1, COR, and COM scores.
Funder
Key Projects of Global Change and Response of Ministry of Science and Technology of China
Subject
General Earth and Planetary Sciences
Reference49 articles.
1. D-linknet: Linknet with pretrained encoder and dilated convolution for high resolution satellite imagery road extraction;Zhou;Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops,2018
2. Fully convolutional networks for semantic segmentation;Long;Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,2015
3. Simultaneous Road Surface and Centerline Extraction From Large-Scale Remote Sensing Images Using CNN-Based Segmentation and Tracing
4. A fusion network for road detection via spatial propagation and spatial transformation
5. BT-RoadNet: A boundary and topologically-aware neural network for road extraction from high-resolution remote sensing imagery
Cited by
5 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献