Year: 2022
Type: article
Abstract: Automatic extraction of road information from remote sensing images is widely used in many fields, such as urban planning and automatic navigation. However, due to interference from noise and occlusion, the existing road extraction methods can easily lead to road discontinuity. To solve this problem, a road extraction network with bidirectional spatial information reasoning (BSIRNet) is proposed, in which neighbourhood feature fusion is used to capture spatial context dependencies and expand the receptive field, and an information processing unit with a recurrent neural network structure is used to capture channel dependencies. BSIRNet enhances the connectivity of road information through spatial information reasoning. Using the public Massachusetts road dataset and Wuhan University road dataset, the superiority of the proposed method is verified by comparing its results with those of other models.
Source: Journal of Sensors
DOI: 10.1155/2022/6391238
Authors: Hai Tan, Hao Xu, Jiguang Dai
Institutions: Ministry of Natural Resources, Liaoning Technical University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $2,830
Awards: 顾及特征差异性的农村区域道路提取方法研究, G5157094141
OpenAlex ID: W4205346352