基于XGBoost算法的遥感图像云检测
Remote Sensing Technology and Application（2023）
长安大学
Abstract
云检测是利用卫星遥感影像进行相关应用的基础.针对云检测过程容易受到复杂地表环境干扰的问题,提出了一种基于极限梯度提升(XGBoost)算法的云检测模型.该方法以TOA反射率、亮温和光谱指数等组建特征空间;然后,采用贝叶斯优化对XGBoost模型的超参数进行了调整.为检验XGBoost的云检测效果,选择不同云场景的Landsat 8遥感影像为测试数据,并把XGBoost、随机森林和决策树的云检测结果作对比.结果表明:本文提出的XGBoost云检测模型的云识别效果优于随机森林和决策树,展现了 XGBoost在云检测中的潜力;且XGBoost的F1分数和Kappa系数分别可达73％和71％以上,实现了较准确的云检测,可为后续开展云检测研究提供一定的支持.
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Key words
Cloud Detection,XGBoost,Random Forest,Decision Tree,Landsat 8
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