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Lookup NU author(s): Dr Shidong WangORCiD, Dr Maria-Valasia PeppaORCiD, Dr Wen Xiao, Professor Jon MillsORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
Climate change is increasing the risk of glacial lake outburst floods (GLOFs) in many of the world’s most vulnerable and high mountain regions. Simultaneously, remote sensing technologies now facilitate continuous monitoring of glacial lake evolution around the globe, although accurate and reliable automated glacial lake mapping from satellite data remains challenging. In this study, a Second-order Attention Network (SoAN) is devised for the automated segmentation of lakes from satellite imagery. In particular, a novel Second-order Attention Module (SoAM) is proposed to capture the long-range spatial dependencies and establish channel attention derived from the covariance representations of local features. Furthermore, as the dimensions of the input and output tensors are identical and it simply relies on matrix calculations, the proposed SoAM can be embedded into different positions of a given architecture while maintaining similar reference speed. The designed network is implemented on Landsat-8 imagery and outputs are compared against representative deep learning models, demonstrating improved results with a Dice of 81.02% and a F2 Score of 85.17%.
Author(s): Wang S, Peppa MV, Xiao W, Maharjan SB, Joshi SP, Mills JP
Publication type: Article
Publication status: Published
Journal: ISPRS Journal of Photogrammetry and Remote Sensing
Year: 2022
Volume: 189
Pages: 289-301
Print publication date: 01/07/2022
Online publication date: 29/05/2022
Acceptance date: 20/05/2022
Date deposited: 14/06/2022
ISSN (electronic): 0924-2716
Publisher: Elseiver
URL: https://doi.org/10.1016/j.isprsjprs.2022.05.007
DOI: 10.1016/j.isprsjprs.2022.05.007
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