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

• Published in: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
• Main Authors: Salman Khaleghian, Habib Ullah, Thomas Kraemer, Torbjorn Eltoft, Andrea Marinoni
• Format: Article in Journal/Newspaper
• Language: English
• Published: IEEE 2021
• Subjects: Deep learning; earth observation; scarce training data; sea ice classification; semisupervised learning (SSL); Ocean engineering; TC1501-1800; Geophysics. Cosmic physics; QC801-809; Sea ice
• Online Access: https://doi.org/10.1109/JSTARS.2021.3119485; https://doaj.org/article/8ed3ec0550f54b1185cfecd9e8ac5aca

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.