SI-STSAR-7: A Large SAR Images Dataset with Spatial and Temporal Information for Classification of Winter Sea Ice in Hudson Bay

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.

Sea Ice Extraction via Remote Sensing Imagery: Algorithms, Datasets, Applications and Challenges

“…However, the collection and annotation of such datasets for sea ice imagery can be challenging and time-consuming. For instance, researchers in [83] highlighted the scarcity of labeled datasets as a major impediment to the development and evaluation of machine-learning algorithms for sea ice classification. Additionally, another limitation arises from the complexity and variability of sea ice characteristics, which can pose challenges for machine-learning models to generalize effectively across different regions and environmental conditions.…”

“…• SI-STSAR-7 [83] The dataset is a spatiotemporal collection of SAR imagery specifically designed for sea ice classification. It encompasses 80 Sentinel-1 A/B SAR scenes captured over two freeze-up periods in Hudson Bay, spanning from October 2019 to May 2020 and from October 2020 to April 2021.…”

E-MPSPNet: Ice–Water SAR Scene Segmentation Based on Multi-Scale Semantic Features and Edge Supervision

“…Khaleghian et al [24] proposed a dataset containing six classes of ice types and ice edge analysis. Song et al [25] established a dataset to facilitate sea-ice classification with spatial and temporal information. However, due to the process of making sea-ice semantic segmentation datasets being complicated and time consuming, only the AI4Arctic/ASIP Sea Ice Dataset [26] is applied to scene segmentation.…”