Generalized Bayesian cloud detection for satellite imagery. Part 2: Technique and validation for daytime imagery
Generalized Bayesian cloud detection for satellite imagery. Part 2: Technique and validation for daytime imagery
复制标题
卫星图像的广义贝叶斯云检测。
DOI：
10.1080/01431160903051711
复制
发表时间：
2010
影响因子：
3.4
通讯作者：
P. Francis
中科院分区：
文献类型：
--
作者：
S. Mackie;C. Merchant;Owen Embury;P. Francis
数值天气预报（NWP）字段用于辅助检测卫星图像中的云。在基于贝叶斯定理的框架内使用基于NWP的模拟观测来计算每个像素的基于物理的概率，其中成像场景是清晰的或多云的。可以对概率设置不同的阈值，以创建特定于应用程序的云掩码。在这里，该技术被证明是适合于白天的应用在陆地和海洋，使用可见光和近红外图像，除了热红外。我们使用的旋转增强可见光和红外成像仪（SEVIRI）实现89%和73%的海洋和陆地，分别使用贝叶斯技术，相比之下，90%和70%，分别为基于阈值的技术与验证数据集的真实技能分数的困难云检测目标的验证数据集。
Numerical Weather Prediction (NWP) fields are used to assist the detection of cloud in satellite imagery. Simulated observations based on NWP are used within a framework based on Bayes' theorem to calculate a physically-based probability of each pixel with an imaged scene being clear or cloudy. Different thresholds can be set on the probabilities to create application-specific cloud masks. Here, the technique is shown to be suitable for daytime applications over land and sea, using visible and near-infrared imagery, in addition to thermal infrared. We use a validation dataset of difficult cloud detection targets for the Spinning Enhanced Visible and Infrared Imager (SEVIRI) achieving true skill scores of 89% and 73% for ocean and land, respectively using the Bayesian technique, compared to 90% and 70%, respectively for the threshold-based techniques associated with the validation dataset.