CloudFCN: Accurate and Robust Cloud Detection for Satellite Imagery with Deep Learning
CloudFCN: Accurate and Robust Cloud Detection for Satellite Imagery with Deep Learning
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DOI：
10.3390/rs11192312
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发表时间：
2019-10
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影响因子：
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通讯作者：
Alistair Francis;P. Sidiropoulos;J. Muller
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作者：
Alistair Francis;P. Sidiropoulos;J. Muller
云掩蔽对地球观测界至关重要。本文研究了高分辨率卫星相机可见光和多光谱图像中的云检测问题。最近，机器学习为云掩蔽问题提供了有前途的解决方案，比传统的阈值技术具有更大的灵活性，传统的阈值技术仅限于具有必要光谱带的仪器。然而，很少有研究使用多尺度特征（如像素级和空间的结合），同时也为现实世界的表现提供令人信服的实验证据。因此，我们引入了基于全卷积网络架构（称为U-net）的CloudFCN，它已成为图像分割的标准深度学习方法。它融合了网络的最浅层和最深层，从而将低级可见内容路由到最深层。我们在这方面提供了广泛的实验，包括来自两个高分辨率传感器（carbonite -2和Landsat 8）的数据以及几个补充测试。由于各种性能增强的设计选择和训练技术，它展示了最先进的性能，可与其他方法相媲美，高速，以及对许多不同地形和传感器类型的鲁棒性。
Cloud masking is of central importance to the Earth Observation community. This paper deals with the problem of detecting clouds in visible and multispectral imagery from high-resolution satellite cameras. Recently, Machine Learning has offered promising solutions to the problem of cloud masking, allowing for more flexibility than traditional thresholding techniques, which are restricted to instruments with the requisite spectral bands. However, few studies use multi-scale features (as in, a combination of pixel-level and spatial) whilst also offering compelling experimental evidence for real-world performance. Therefore, we introduce CloudFCN, based on a Fully Convolutional Network architecture, known as U-net, which has become a standard Deep Learning approach to image segmentation. It fuses the shallowest and deepest layers of the network, thus routing low-level visible content to its deepest layers. We offer an extensive range of experiments on this, including data from two high-resolution sensors—Carbonite-2 and Landsat 8—and several complementary tests. Owing to a variety of performance-enhancing design choices and training techniques, it exhibits state-of-the-art performance where comparable to other methods, high speed, and robustness to many different terrains and sensor types.