Fmask 4.0: Improved cloud and cloud shadow detection in Landsats 4-8 and Sentinel-2 imagery
Fmask 4.0: Improved cloud and cloud shadow detection in Landsats 4-8 and Sentinel-2 imagery
复制标题
Fmask 4.0：改进了 Landsats 4-8 和 Sentinel-2 图像中的云和云阴影检测
DOI：
10.1016/j.rse.2019.05.024
复制
发表时间：
2019-09-15
影响因子：
13.5
通讯作者：
He, Binbin
中科院分区：
文献类型：
--
作者：
Qiu, Shi;Zhu, Zhe;He, Binbin
我们开发了掩模函数（Fmask）4.0算法，用于Landsat 4-8和Sentinel-2图像中的自动云和云阴影检测。主要有三个创新性改进：（1）辅助数据的整合，其中全球地表水发生（GSWO）数据用于改善陆地和水的分离，全球数字高程模型（DEM）用于归一化热和卷云带;（2）发展新的云概率，其中设计了基于雾度优化变换（HOT）的云概率来代替Sentinel-2图像的温度概率，并针对全球参考数据集对不同传感器的云概率进行组合和重新校准;以及（3）利用光谱背景特征，其中创建光谱背景雪指数（SCSI）以更好地区分极地地区的雪/冰与云，并且应用基于形态学的方法来减少明亮陆地表面中的调试误差（例如，城市/建筑物和山区雪/冰）。与3.3版本相比，Fmask 4.0算法对Landsat 4-8图像显示出更高的总体精度（Zhu等人，2015年）（Landsat 4-7为92.40%对90.73%，Landsat 8为94.59%对93.30%），并且Sentinel-2图像的总体准确度比Sen 2Cor算法的2.5.5版本高得多（Mtiller-Wilm等人，2018年）（94.30%对87.10%）。
We developed the Function of mask (Fmask) 4.0 algorithm for automated cloud and cloud shadow detection in Landsats 4-8 and Sentinel-2 images. Three major innovative improvements were made as follows: (1) integration of auxiliary data, where Global Surface Water Occurrence (GSWO) data was used to improve the separation of land and water, and a global Digital Elevation Model (DEM) was used to normalize thermal and cirrus bands; (2) development of new cloud probabilities, in which a Haze Optimized Transformation (HOT)-based cloud probability was designed to replace temperature probability for Sentinel-2 images, and cloud probabilities were combined and re-calibrated for different sensors against a global reference dataset; and (3) utilization of spectral contextual features, where a Spectral-Contextual Snow Index (SCSI) was created for better distinguishing snow/ice from clouds in polar regions, and a morphology -based approach was applied to reduce the commission error in bright land surfaces (e.g., urban/built-up and mountain snow/ice). The Fmask 4.0 algorithm showed higher overall accuracies for Landsats 4-8 imagery than the 3.3 version (Zhu et al., 2015) (92.40% versus 90.73% for Landsats 4-7 and 94.59% versus 93.30% for Landsat 8), and much higher overall accuracies for Sentinel-2 imagery than the 2.5.5 version of the Sen2Cor algorithm (Mtiller-Wilm et al., 2018) (94.30% versus 87.10%).