基于机器学习的偏振遥感云检测优化算法

Optimization Algorithm for Polarization Remote Sensing Cloud Detection Based on Machine Learning

偏振遥感经验阈值云检测算法受主观因素影响较强,极易在亮地表上空出现云检测不准确的问题。针对该问题,本文提出了一种主动和被动遥感卫星相结合的机器学习云检测算法。该算法基于POLDER3载荷多通道多角度偏振特性以及CALIOP载荷高精度云垂直特性展开研究,利用POLDER3载荷和CALIOP载荷观测重合区域数据,搭建了粒子群算法优化的BP神经网络训练云检测模型。基于该云检测训练模型,利用POLDER3一级数据开展云检测试验,试验显示该算法云检测结果与MODIS云检测产品一致性为92.46%,高于POLDER3官方云检测产品与MODIS云检测产品的一致性83.13%。通过对比本文算法试验结果与POLDER3官方云检测产品不同的像元的光学特性发现,相比POLDER3官方算法,本算法对于亮地表上空薄云具有更强的敏感性,能更有效地进行云检测。

The polarization remote sensing experience threshold cloud detection algorithm is strongly affected by subjective factors,and it is very easy to have the problem of inaccurate cloud detection over bright ground.In response to this problem,this paper proposes a machine learning cloud detection algorithm that combines active and passive remote sensing satellites.The algorithm is based on the multichannel multi-angle polarization characteristics of the POLDER3 payload and the high-precision cloud vertical characteristics of the CALIOP payload.It uses POLDER3 payload and CALIOP.The load observation overlaps the regional data,and the BP neural network optimized by the Particle Swarm Optimization algorithm is built to train the cloud detection model.Based on the cloud detection training model,a cloud detection experiment was carried out using POLDER3 level-1 data.The experiment showed that the cloud detection result of this algorithm is 92.46%consistent with the MODIS cloud detection product,which is higher than the consistency between the official POLDER3 cloud detection product and the MODIS cloud detection product 83.13%.By comparing the experimental results of the algorithm in this paper with the optical characteristics of different pixels from the official POLDER3 cloud detection product,it is found that compared with the official POLDER3 algorithm,this algorithm is more sensitive to thin clouds over the bright surface and can perform cloud detection more effectively.