通过对风云四号每两张相隔15分钟的图像进行分析, 提出了归一化动云指数, 加强数据集对低云、薄云及云系运动边缘的检测.在此基础上, 提出了一种基于归一化动云指数的动态阈值法用于初步云检测, 并进一步提出基于BP神经网络的云检测算法.实验结果表明, 该算法可以消除阈值选取中的主观影响, 在大范围复杂下垫面的遥感图像数据中可以取得较好的云检测效果.

In this paper, normalized difference cloud moving index (NDCMI) is put forward by analyzing the remote sensing data every 15 minutes. By applying NDCMI, the detection of low cloud, thin cloud and the edge of moving cloud can be enhanced. We proposed a dynamic threshold method with NDCMI for preliminary cloud detection, and then through the preliminary results, we proposed a novel cloud detection method based on back propagation neural network (BP-NN). The experimental results show that the proposed method can eliminate the subjectivity in threshold selection, and can achieve better cloud detection results for remote sensing image in complex situations.