基于集成学习的风云四号遥感图像云相态分类算法

Ensemble-learning-based Cloud Phase Classification Method for FengYun-4 Remote Sensing Images

云相态分类在气象预报和气候研究中具有重要的地位。我国新一代气象卫星风云四号的成像仪在光谱通道数量和空间分辨率较上一代风云二号有较大提升,这为云相态的研究提供了新的遥感数据。本文首先对风云四号相隔15 min的遥感图像进行分析,然后提出亮温云相态指数,该指数可以进行初步云相态分类,最后在此基础上提出基于集成学习的云相态分类算法。实验结果与风云四号官方云相态分类结果进行比较,水云的一致率达到91.69%,冰云的一致率达到76.10%。

Cloud phase classification plays an important role in meteorological forecast and climate research.The image of meteorological satellite FengYun-4(FY-4)has more channels and better resolution than FY-2.So it provides new remote sensing data for the study of the cloud phase.This study uses a brightness temperature cloud phase index to obtain cloud phase data.Thereafter,using the cloud phase data and ensemble learning algorithm,we develop a cloud phase classification model.By applying the cloud phase classification model,the predicted classification accuracy of water cloud and ice cloud are 91.69%and 76.10%,respectively.