SeaIceWeather
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SeaIceWeather Dataset This is the SeaIceWeather dataset, collected for training and evaluation of deep learning based de-weathering models. This dataset is linked to our paper titled: Deep Learning Strategies for Analysis of Weather-Degraded Optical Sea Ice Images. The paper can be accessed at: https://doi.org/10.1109/jsen.2024.3376518. Abstract of the paper: Ship-based sea ice analysis algorithms rely on optical images captured in optimal weather conditions with high visibility. However, Arctic imagery is often affected by weather-related degradation due to haze, snow, and rain, impacting the efficacy of deep learning tasks for sea ice analysis, such as segmentation and classification. This article introduces and evaluates two strategies to address weather-induced degradation in optical sea ice images (RGB). Strategy 1 employs a two-step pipeline: first, removal of weather degradation using deep learning-based de-weathering algorithms, and then, analysis of images as a part of sea ice segmentation/classification tasks. Strategy 2 proposes a “weather as augmentation” training approach to create all-in-one weather-resilient segmentation and classification models. Furthermore, we introduce the first open-source ice image dataset (SeaIceWeather) with paired images—one clean and one weather-degraded. Such a dataset allows for training and validation of supervised deep learning-based de-weathering algorithms. Using this dataset, we show that the proposed strategies are effective against weather-degraded images, achieving parity with the segmentation and classification performance on clean images. In addition, we demonstrate de-weathering models capable of removing degradations due to four different weather conditions, including rain, haze, snow, and raindrops on a camera lens with a single set of weights. The presented strategies lay the foundation for robust shipborne sea ice analysis systems resilient to adverse weather conditions. Furthermore, we hope that the dataset introduced in this study ignites further interest in the analysis of weather-degraded sea ice images.
海冰气象数据集（SeaIceWeather Dataset） 本数据集专为基于深度学习的去气象退化模型的训练与评估采集构建。 本数据集与题为《面向气象退化光学海冰图像分析的深度学习策略》的学术论文相关联，该论文可通过以下链接获取：https://doi.org/10.1109/jsen.2024.3376518。 论文摘要如下： 船基海冰分析算法通常依赖于最佳气象条件下拍摄的高能见度光学图像。然而，北极海域图像常受雾霭、降雪与降雨引发的气象退化影响，进而干扰海冰分析相关深度学习任务（如图像分割与分类）的执行效能。本文针对RGB格式光学海冰图像的气象退化问题，提出并评估了两种解决方案。策略1采用两步式流程：首先通过基于深度学习的去气象退化算法消除图像中的气象干扰，随后将处理后的图像用于海冰分割或分类任务。策略2则提出“将气象干扰作为数据增强”的训练方案，以构建兼具气象鲁棒性的一体化分割与分类模型。此外，本文还发布了首个开源海冰图像数据集（SeaIceWeather Dataset），该数据集包含成对图像：一张为清晰无干扰原图，另一张为带有气象退化的图像。此类数据集可用于监督式深度学习去气象退化算法的训练与验证。依托本数据集，我们验证了所提策略的有效性：其在气象退化图像上的分割与分类性能可媲美清晰图像上的表现。此外，我们还证明了单一组权重的去气象退化模型即可消除四种不同气象条件下的图像退化，包括降雨、雾霭、降雪以及相机镜头上的雨滴。所提策略为具备恶劣气象鲁棒性的船基海冰分析系统奠定了技术基础。 最后，我们希望本研究发布的数据集能够激发学界对气象退化海冰图像分析领域的进一步研究兴趣。
