Snow Coverage Mapping by Learning from Sentinel-2 Satellite Multispectral Images via Machine Learning Algorithms
Snow Coverage Mapping by Learning from Sentinel-2 Satellite Multispectral Images via Machine Learning Algorithms
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DOI：
10.3390/rs14030782
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发表时间：
2022-02
期刊：
影响因子：
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通讯作者：
Yucheng Wang;Jinya Su;Xiaojun Zhai;Fanlin Meng;Cunjia Liu
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文献类型：
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作者：
Yucheng Wang;Jinya Su;Xiaojun Zhai;Fanlin Meng;Cunjia Liu
积雪覆盖图不仅在水文学和气候学研究中起着至关重要的作用，而且在研究作物越冬病害以实现农业智能管理方面也起着至关重要的作用。这项工作通过机器学习方法从哨兵2号卫星多光谱图像中学习，研究了积雪覆盖图。为此，首先收集雪覆盖图的最大数据集(据我们所知)，其中包括三个典型类别(雪、云和背景)，并通过QGIS中的半自动分类插件进行标注。然后，将基于随机森林的传统机器学习和基于U-Net的深度学习应用于语义分割的挑战。还研究了各种输入频带组合的影响，以便确定最合适的输入频带组合。实验结果表明：(1)传统的机器学习方法和改进的深度学习方法在雪图绘制中的性能都明显优于现有的基于规则的Sen2Cor产品；(2)U-net通过卷积运算将光谱和空间信息结合到U-net中，总体上优于随机森林；(3)U-net的最佳光谱波段组合是B2、B11、B4和B9。结果表明，基于U网的具有4个信息波段的深度学习分类器适用于雪覆盖图的绘制。
Snow coverage mapping plays a vital role not only in studying hydrology and climatology, but also in investigating crop disease overwintering for smart agriculture management. This work investigates snow coverage mapping by learning from Sentinel-2 satellite multispectral images via machine-learning methods. To this end, the largest dataset for snow coverage mapping (to our best knowledge) with three typical classes (snow, cloud and background) is first collected and labeled via the semi-automatic classification plugin in QGIS. Then, both random forest-based conventional machine learning and U-Net-based deep learning are applied to the semantic segmentation challenge in this work. The effects of various input band combinations are also investigated so that the most suitable one can be identified. Experimental results show that (1) both conventional machine-learning and advanced deep-learning methods significantly outperform the existing rule-based Sen2Cor product for snow mapping; (2) U-Net generally outperforms the random forest since both spectral and spatial information is incorporated in U-Net via convolution operations; (3) the best spectral band combination for U-Net is B2, B11, B4 and B9. It is concluded that a U-Net-based deep-learning classifier with four informative spectral bands is suitable for snow coverage mapping.