BSIRNet: A Road Extraction Network with Bidirectional Spatial Information Reasoning. (12th January 2022)

• Record Type: Journal Article
• Authors: Tan, Hai; Xu, Hao; Dai, Jiguang
• Other Names: Zhang Wei Academic Editor.
• Is Part Of: Journal of sensors. Volume 2022(2022)
• Publication Date: 2022-01-12
• Subjects: Detectors -- Periodicals; 681.205
• DOI: 10.1155/2022/6391238
• Languages: English
• ISSNs: 1687-725X

Abstract

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.