摘要
利用Sentinel-2遥感卫星影像,结合遥感优势以光谱、指数、纹理等14种多种特征信息为输入,依托贝叶斯优化算法,设计了一种能自动获取最优超参数组合的BO-XGBoost方法,并将其成功应用于2021年阳澄湖蓝藻信息提取。结果表明:①通过贝叶斯优化算法获取最优超参数组合,进行训练得到BO-XGBoost蓝藻分类模型,其训练结果在测试集和训练集上表现效果良好,准确率高达96.07%;②将BO-XGBoost应用于参与样本集构建的影像,其蓝藻识别结果与人工解译成果对比,2种方法得到的蓝藻空间分布情况基本一致,交并比最低为41.31%;③为评价该分类模型在其他时相的适用性,选择其他时相影像数据进行蓝藻提取,BO-XGBoost与人工解译2种方法蓝藻空间分布情况基本一致,交并比最低为43.85%。
With 14 types of multi-feature information,such as spectrum,index,and texture,of remote sensing images from satellite Sentinel-2 as input and using the Bayesian optimization algorithm,this study designed the BO-XGBoost method used to automatically obtain the optimal hyperparameter combination.This method was successfully applied to the information extraction of cyanobacteria in Yangcheng Lake in 2021.The results show that:①The optimal hyperparameter combination was obtained using the Bayesian optimization algorithm,and then the BO-XGBoost cyanobacteria classification model was established through obtaining.The training results performed well on the test and training sets,with an accuracy rate of up to 96.07%;②The BO-XGBoost method was applied to the images used in the sample set.The comparison between the cyanobacteria identification results and the manual interpretation results shows that the two methods yielded roughly the same spatial distribution of cyanobacteria,with a lowest intersection over union(IoU)of 41.31%;③To evaluate the applicability of the BO-XGBoost method in other periods,images of other periods were selected for the information extraction of cyanobacteria.As a result,both BO-XGBoost and manual interpretation also yielded roughly the same spatial distribution of cyanobacteria,with a lowest IoU of 43.85%.
作者
田晨
张金龙
金义蓉
董世元
王彬
张乃祥
TIAN Chen;ZHANG Jinlong;JIN Yirong;DONG Shiyuan;WANG Bin;ZHANG Naixiang(Suzhou Water Conservancy and Water Information Dispatching Command Center,Suzhou 215011,China;Suzhou Land Think Software Technology Company Limited of Chinese Academy of Science,Suzhou 215163,China)
出处
《自然资源遥感》
CSCD
北大核心
2023年第1期49-56,共8页
Remote Sensing for Natural Resources
基金
苏州市水利水务科技项目“基于光学与SAR联合观测的湖泊凤眼莲分布自动化提取研究”(编号:2021009)资助。
