Work

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

Year: 2021

Type: article

Abstract: In this paper, we propose a novel deep semi-supervised learning (SSL) method namely Teacher-Student based label propagation method (TSLP-SSL) for sea ice classification based on Sentinel-1 synthetic aperture radar (SAR) 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 limited number of labeled samples and relatively large number of unlabeled samples. Therefore, our method addresses the key challenge of using 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 semi-supervised reference methods.

Source: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

DOI: 10.1109/jstars.2021.3119485

Authors: Salman Khaleghian, Habib Ullah, Thomas Kraemer, Torbjorn Eltoft, Andrea Marinoni

Institutions: UiT The Arctic University of Norway, Norwegian University of Life Sciences

Language: English

Topic: Arctic and Antarctic ice dynamics

Subfield: Atmospheric Science

Field: Earth and Planetary Sciences

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,750

Funders: Universitetet i Tromsø, European Commission, Norges Forskningsråd