Abstract
Remote sensing satellites have been broadly applied to sea ice monitoring. The substantial increase in satellite imagery provides a large amount of data support for deep learning methods in the sea ice classification field. However, there is a lack of public remote sensing datasets to facilitate sea ice classification with spatial and temporal information and to benchmark the deep learning methods. In this paper, we provide a labeled large sea ice dataset derived from time-series sentinel-1 SAR images, dubbed SI-STSAR-7, and a validated dataset construction method for sea ice classification research. The SI-STSAR-7 dataset includes seven different sea ice types corresponding to different sea ice development stages in Hudson Bay during winter, and its samples are time sequences of SAR image patches in order to embody the differences of backscattering intensity and textures between different sea ice types, as well as the change of sea ice with time. We construct the dataset by first performing noise reduction and mitigation of incidence angle dependence on SAR images, and then producing data samples and labeling them based on our proposed sample-producing principles and the weekly regional ice charts provided by Canadian Ice Service. Three baseline classification methods are developed on SI-STSAR-7 to establish benchmarks, which are evaluated with accuracy and kappa coefficient. The sample-producing principles are verified through experiments. Based on the experimental results, sea ice classification can be implemented well on SI-STSAR-7.
Highlights
IntroductionSea ice can directly affect human activities such as ship navigation and seabed mining and may even lead to major disasters [2]
Publisher’s Note: MDPI stays neutralSea ice monitoring in polar regions is very important because sea ice has a great impact on ocean hydrological conditions, atmospheric circulation, and climate change [1].sea ice can directly affect human activities such as ship navigation and seabed mining and may even lead to major disasters [2]
We found that the overall performance of the deep learning methods (ConvLSTM and Convolutional Neural Network (CNN)) is better than the machine learning method (SVM)
Summary
Sea ice can directly affect human activities such as ship navigation and seabed mining and may even lead to major disasters [2]. Many studies on sea ice classification have been carried out using Gabor wavelet techniques [4], Markov random field (MRF) [5,6], neural network [7,8], support vector machine (SVM) [9,10], and other methods. With the widespread application of deep learning in the image field, researchers have applied deep learning models to sea ice classification and achieved high classification accuracy [11,12]. Some public remote sensing image datasets have been developed for ground object detection [13,14], ship identification [15], and oil well detection [16]. In the field of sea ice, the products of sea ice extent with regard to jurisdictional claims in published maps and institutional affiliations
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