Improvement of the Fmask algorithm for Sentinel-2 images: Separating clouds from bright surfaces based on parallax effects
Improvement of the Fmask algorithm for Sentinel-2 images: Separating clouds from bright surfaces based on parallax effects
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DOI：
10.1016/j.rse.2018.04.046
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发表时间：
2018-09-15
影响因子：
13.5
通讯作者：
Hill, Joachim
中科院分区：
文献类型：
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作者：
Frantz, David;Hass, Erik;Hill, Joachim
云的可靠识别对于任何类型的光学遥感图像分析都是必要的，特别是在操作和全自动设置中。最详细和最广泛的算法之一，即Fmask，最初是为Landsat卫星套件开发的。尽管它们很相似，但由于没有热波段，目前对哨兵-2号图像的应用受到阻碍，虽然考虑到卷云波段可以改善结果，但哨兵-2号云探测在两点上不能令人满意。(1)在卷云带中，低空云可能无法检测到，（2）当只考虑光谱信息时，明亮的陆地表面-特别是建筑结构-经常被错误地分类为云。在本文中，我们提出了云位移指数（CDI），它利用了三个高度相关的近红外波段，观察到不同的视角。因此，像云这样的高架物体可以在视差下观察到，并且可以可靠地与明亮的地面物体分离。我们比较CDI与目前使用的云概率，并提出如何将这个新的功能集成到Fmask算法。我们使用覆盖各种全球环境和气候的大都市地区的测试图像来验证该方法，表明云和建筑结构的成功分离（总体精度95%，即与20个测试站点的先前Fmask版本相比，总体精度提高了0.29-0.39），因此完全补偿了缺失的热带。
Reliable identification of clouds is necessary for any type of optical remote sensing image analysis, especially in operational and fully automatic setups. One of the most elaborated and widespread algorithms, namely Fmask, was initially developed for the Landsat suite of satellites. Despite their similarity, application to Sentinel-2 imagery is currently hampered by the unavailability of a thermal band, and although results can be improved when taking the cirrus band into account, Sentinel-2 cloud detections are unsatisfactory in two points. (1) Low altitude clouds can be undetectable in the cirrus band, and (2) bright land surfaces - especially built-up structures - are often misclassified as clouds when only considering spectral information. In this paper, we present the Cloud Displacement Index (CDI), which makes use of the three highly correlated near infrared bands that are observed with different view angles. Hence, elevated objects like clouds are observed under a parallax and can be reliably separated from bright ground objects. We compare CDI with the currently used cloud probabilities, and propose how to integrate this new functionality into the Fmask algorithm. We validate the approach using test images over metropolitan areas covering a wide variety of global environments and climates, indicating the successful separation of clouds and built-up structures (overall accuracy 95%, i.e. an improvement in overall accuracy of 0.29-0.39 compared to the previous Fmask versions over the 20 test sites), and hence a full compensation for a missing thermal band.