论文标题
地面红外图像中云分割方法的比较分析
Comparative Analysis of Methods for Cloud Segmentation in Ground-Based Infrared Images
论文作者
论文摘要
光伏系统在电网中的渗透增加使其容易受到云阴影投影的影响。地面红外图像中的实时云分割对于降低全球太阳辐照度预测的噪声非常重要。我们提出了用于云分割的判别模型和生成模型之间的比较。评估了云分割中监督和无监督学习方法的性能。在原始公式中解决了判别模型，以使其在实时应用中可行。使用J统计量比较表演。红外图像预处理以去除固定伪影可以提高分析方法中的整体性能。在特征向量中包含来自相邻像素的功能会导致某些情况下的性能提高。马尔可夫随机字段在无监督和监督的生成模型中都取得了最佳性能。在原始中解决的判别模型产生的计算时间大大较低，并且在分割中的高性能。当将预处理应用于红外图像时，生成和判别模型是可比的。
The increasing penetration of photovoltaic systems in the power grid makes it vulnerable to cloud shadow projection. Real-time cloud segmentation in ground-based infrared images is important to reduce the noise in intra-hour global solar irradiance forecasting. We present a comparison between discriminative and generative models for cloud segmentation. The performances of supervised and unsupervised learning methods in cloud segmentation are evaluated. The discriminative models are solved in the primal formulation to make them feasible in real-time applications. The performances are compared using the j-statistic. Infrared image preprocessing to remove stationary artifacts increases the overall performance in the analyzed methods. The inclusion of features from neighboring pixels in the feature vectors leads to a performance improvement in some of the cases. Markov Random Fields achieve the best performance in both unsupervised and supervised generative models. Discriminative models solved in the primal yield a dramatically lower computing time along with high performance in the segmentation. Generative and discriminative models are comparable when preprocessing is applied to the infrared images.
