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A second-order attention network for glacial lake segmentation from remotely sensed imagery

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%.

Publication metadata

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


DOI: 10.1016/j.isprsjprs.2022.05.007


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Funder referenceFunder name
NE/S005919/1Natural Environment Research Council (NERC)