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.
Highlights
A RCTIC sea ice keeps the northern polar regions cool and thereby helps to moderate the global climate
We proposed a teacher–student-based label propagation method for sea ice classification
The pseudo-labels from the teacher models were fed to the student model during the training to find an unbiased decision boundary
Summary
A RCTIC sea ice keeps the northern polar regions cool and thereby helps to moderate the global climate. DLMs, when properly trained on large training datasets, have shown excellent generalization capabilities in many research fields, including several remote sensing applications such as food security monitoring [11], hybrid data-driven Earth observation modeling [12], and flood mapping from high-resolution optical data [13] We consider these achievements in the aforementioned fields and believe that deep neural networks (DNNs) may show performance improvement in automatic sea ice classification [14], [15]. In the past few years, semisupervised models have presented performance improvement in various fields of remote sensing research, such as despeckling of SAR images [19], change detection in heterogeneous remote sensing images [20], and hyperspectral image classification [21] Considering these successes, we anticipate that deep SSL methodologies could be favorable in sea ice classification and potentially lead to significant improvements by overcoming the specific challenge of few labeled samples.
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