BS-Net: Using Joint-Learning Boundary and Segmentation Network for Coastline Extraction from Remote Sensing Images. Issue 12 (2nd December 2021)

• Record Type: Journal Article
• Authors: Jing, Wei; Cui, Binge; Lu, Yan; Huang, Ling
• Is Part Of: Remote sensing letters. Volume 12:Issue 12(2021)
• Page Start: 1260
• Page End: 1268
• Publication Date: 2021-12-02
• Subjects: Remote sensing -- Periodicals; Remote sensing; Periodicals; 621.3678
• DOI: 10.1080/2150704X.2021.1979271
• Languages: English
• ISSNs: 2150-704X

Abstract

ABSTRACT: The coastline extraction from remote-sensing images is of great significance to the dynamic monitoring of the coastal zone. The types of coastlines are complex and diverse, and they show different spectrum, texture, and shape features, so accurately extracting coastlines is still a challenging task. The semantic segmentation model based on deep learning has good generalization ability. However, the down sampling operation will lose the location of boundary information, resulting in the location offset between the extracted coastlines and the actual coastlines. A multi-task network, called the joint learning network of boundary and segmentation (BS-Net), was proposed in this letter. BS-Net adds a coastline positioning stream to supervise the location of the coastlines. Moreover, this letter designed a boundary-segmentation interaction (BSI) module for the mutual guidance of information between the coastline positioning stream and the sea-land segmentation stream to correct the coastline features and enhance the segmentation boundary. The experimental results on a set of Gaofen-1 remote sensing images showed that, for various natural coastlines and artificial coastlines, coastlines extracted based on BS-Net were more accurate than those extracted by other methods. Code is available at: https://github.com/weiAI1996/BS-Net .