基于可见光红外成像辐射仪多波段、宽覆盖、长重访周期的特点,以及云层在可见光到热红外通道的分布及变化特性,提出了一种适用于可见光红外成像辐射仪数据的改进的动态阈值云检测算法;通过遥感目视解译的方法对云检测结果进行精度验证,并与通用动态阈值云检测算法、可见光红外成像辐射仪云掩膜产品的结果进行对比。结果表明:所提算法能以较高的精度识别不同地表上空的云层,平均总体精度为93.23%,平均Kappa系数为0.821,对薄、碎云的整体识别精度得到了明显提高,错分和漏分误差明显减小;所提算法的云检测结果整体优于通用动态阈值云检测算法和可见光红外成像辐射仪云掩膜产品的云检测结果。

Herein, we propose an improved dynamic threshold cloud detection algorithm (I-DTCDA) for visible infrared imaging radiometers (VIIRS) based on the multi-channel, wide coverage, and short revisit period features of a VIIRS. In addition, the algorithm is also based on the characteristics of the cloud distributions and variations in the visible and thermal infrared channels. We validated the accuracy of the cloud detection results using the remote sensing visual interpretation method. We compared our results with those using the universal dynamic threshold cloud detection algorithm (UDTCDA) and the VIIRS cloud mask (VCM) products. The results show that the proposed algorithm has average overall accuracy of 93% (Kappa=0.821) over different surface features. In particular, for the thin and broken clouds, the overall accuracy is significantly improved and the commission and omission errors are obviously reduced. The cloud detection results using the proposed algorithm are superior to those using UDTCDA and VCM.