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Deep Semisupervised Teacher–Student Model Based on Label Propagation for Sea Ice Classification

Permanent link
https://hdl.handle.net/10037/24319
DOI
https://doi.org/10.1109/JSTARS.2021.3119485
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Date
2021-10-14
Type
Journal article
Tidsskriftartikkel
Peer reviewed

Author
Khaleghian, Salman; Ullah, Habib; Kræmer, Thomas; Eltoft, Torbjørn; Marinoni, Andrea
Abstract
In this article, we propose a novelteacher–student-based label propagation deep semisupervised learning (TSLP-SSL) method for sea ice classification based on Sentinel-1 synthetic aperture radar data. For sea ice classification, labeling the data precisely is very time consuming and requires expert knowledge. Our method efficiently learns sea ice characteristics from a limited number of labeled samples and a relatively large number of unlabeled samples. Therefore, our method addresses the key challenge of using a limited number of precisely labeled samples to achieve generalization capability by discovering the underlying sea ice characteristics also from unlabeled data. We perform experimental analysis considering a standard dataset consisting of properly labeled sea ice data spanning over different time slots of the year. Both qualitative and quantitative results obtained on this dataset show that our proposed TSLP-SSL method outperforms deep supervised and semisupervised reference methods.
Is part of
Khaleghian, S. (2022). Scalable computing for earth observation - Application on Sea Ice analysis. (Doctoral thesis). https://hdl.handle.net/10037/27513.
Publisher
IEEE
Citation
Khaleghian, Ullah, Kræmer, Eltoft, Marinoni. Deep Semisupervised Teacher–Student Model Based on Label Propagation for Sea Ice Classification . IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2021;14:10761-10772
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