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Lookup NU author(s): Dr Jin XingORCiD, Professor Zhenhong Li
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND).
© 2021 The AuthorsAutomatic river bridge detection is a typical and valuable application for SAR image analysis. However, the background information of SAR image is complex, and there are many specious targets with similar features, such as road, ponds and ridges, which usually cause false alarms. And current river bridge detection methods fail to handle these interference efficiently. Therefore, this paper applies deep learning to SAR and proposes a new river bridge detection algorithm, which is named as Single Short Detection-Adaptively Effective Feature Fusion (SSD-AEFF). It can effectively reduce the interference of noisy information, and achieve fast and high-precision detection of river bridges in complex SAR imagery. SSD-AEFF is based on SSD, and AEFF module is innovated to enhance the multi-scale feature maps together with effective Squeeze-Excitation (eSE) module to further fuse effective features and decrease the interference of background information. Additionally, Non-Maximum Suppression (NMS) is used to screen out redundant candidate boxes to produce the final detection result. Moreover, Gradient Harmonizing Mechanism (GHM) loss function is introduced to solve the problem of sample imbalance in the training process. Experimental results on TerraSAR data compared with existing baseline models demonstrate the superiority of the proposed SSD-AEFF algorithm.
Author(s): Chen L, Weng T, Xing J, Li Z, Yuan Z, Pan Z, Tan S, Luo R
Publication type: Article
Publication status: Published
Journal: International Journal of Applied Earth Observation and Geoinformation
Year: 2021
Volume: 102
Print publication date: 01/10/2021
Online publication date: 03/07/2021
Acceptance date: 26/06/2021
Date deposited: 06/01/2022
ISSN (print): 1569-8432
ISSN (electronic): 1872-826X
Publisher: Elsevier B.V.
URL: https://doi.org/10.1016/j.jag.2021.102425
DOI: 10.1016/j.jag.2021.102425
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