Cloud detection algorithm comparison and validation for operational Landsat data products
Cloud detection algorithm comparison and validation for operational Landsat data products
复制标题
DOI：
10.1016/j.rse.2017.03.026
复制
发表时间：
2017-06-01
影响因子：
13.5
通讯作者：
Laue, Brady
中科院分区：
文献类型：
--
作者：
Foga, Steve;Scaramuzza, Pat L.;Laue, Brady
云是星载光学图像中普遍存在且不可避免的问题。准确的、有据可查的和自动化的云检测算法是有效利用大量遥感数据的必要条件。Landsat项目特别适合于云评估算法的比较验证，因为Landsat地面系统的模块化结构允许对新代码进行快速评估，并且因为Landsat拥有当前任何卫星数据档案中最全面的手动真值掩模。目前，Landsat Level-1产品生成系统（LPGS）使用单独的算法来确定云、卷云和雪和/或冰的概率。随着Landsat 8操作陆地成像仪（OLI）/热红外传感器（TIRS）卫星上的更多波段以及更多的云遮蔽算法，美国地质调查局（USGS）正在用一种更强大的算法取代当前的云遮蔽工作流程，该算法能够以最小的修改在多个Landsat传感器上工作。由于TIRS的杂散光和间歇性数据可用性的固有误差，这些算法需要在有和没有热数据的情况下操作。在这项研究中，我们创建了一个工作流程，以评估云和云阴影掩蔽算法，使用手动来自Landsat 7增强型专题制图仪（ETM+）和Landsat 8 OLI/FIRS数据的云验证掩模。我们创建了一个新的验证数据集，由96个Landsat 8场景组成，代表不同的生物群落和云层覆盖比例。我们通过云和云阴影的整体准确性、遗漏误差和委托误差来评估算法性能。我们发现，CFMask，C代码的基础上的功能的面具（Fmask）算法，其置信带具有最好的整体准确性的许多算法测试使用我们的验证数据。人工热自动云覆盖算法（AT-ACCA）是最精确的非热算法。我们优先考虑CFMask用于操作云和云阴影检测，因为它来自物理现象的先验知识，并且可在没有地理限制的情况下操作，使其适用于当前和未来的陆地成像任务，而无需在机器学习环境中重新训练。(C)2017爱思唯尔公司All rights reserved.
Clouds are a pervasive and unavoidable issue in satellite-borne optical imagery. Accurate, well-documented, and automated cloud detection algorithms are necessary to effectively leverage large collections of remotely sensed data. The Landsat project is uniquely suited for comparative validation of cloud assessment algorithms because the modular architecture of the Landsat ground system allows for quick evaluation of new code, and because Landsat has the most comprehensive manual truth masks of any current satellite data archive. Currently, the Landsat Level-1 Product Generation System (LPGS) uses separate algorithms for determining clouds, cirrus clouds, and snow and/or ice probability on a per-pixel basis. With more bands onboard the Landsat 8 Operational Land Imager (OLI)/rhermal Infrared Sensor (TIRS) satellite, and a greater number of cloud masking algorithms, the U.S. Geological Survey (USGS) is replacing the current cloud masking workflow with a more robust algorithm that is capable of working across multiple Landsat sensors with minimal modification. Because of the inherent error from stray light and intermittent data availability of TIRS, these algorithms need to operate both with and without thermal data. In this study, we created a workflow to evaluate cloud and cloud shadow masking algorithms using cloud validation masks manually derived from both Landsat 7 Enhanced Thematic Mapper Plus (ETM +) and Landsat 8 OLI/FIRS data. We created a new validation dataset consisting of 96 Landsat 8 scenes, representing different biomes and proportions of cloud cover. We evaluated algorithm performance by overall accuracy, omission error, and commission error for both cloud and cloud shadow. We found that CFMask, C code based on the Function of Mask (Fmask) algorithm, and its confidence bands have the best overall accuracy among the many algorithms tested using our validation data. The Artificial Thermal-Automated Cloud Cover Algorithm (AT-ACCA) is the most accurate nonthermal-based algorithm. We give preference to CFMask for operational cloud and cloud shadow detection, as it is derived from a priori knowledge of physical phenomena and is operable without geographic restriction, making it useful for current and future land imaging missions without having to be retrained in a machine-learning environment. (C) 2017 Elsevier Inc. All rights reserved.