自然资源遥感2024，Vol.36Issue(3)：240-247,8.DOI:10.6046/zrzyyg.2023135
Landsat和GF数据面向对象土地覆盖分类研究
Exploring the object-oriented land cover classification based on Landsat and GF data
摘要
Abstract
This study aims to explore the object-oriented classification based on moderate-resolution remote sensing data.Using the Landsat8 OLI,Landsat5 TM,and GF1 data obtained from the northern mountainous area and the southern plain area in Hebei Province,this study compared the land cover classification effects of four classifiers:support vector machine(SVM),random forest(RF),decision tree(DT),and naive Bayes(NB).Moreover,it analyzed the impacts of critical parameters in SVM,RF,and DT on the classification results.The findings indicate that the classification results of the classifiers vary slightly in the two study areas,with their effects decreased in the order of SVM,NB,RF,and DT.The classification accuracies of SVM and DT fluctuated significantly with parameter changes.With C values not below 103 and gamma values not exceeding 10-1,SVM can yield classification accuracies above 90％in all cases.With depth values over 3,DT exhibits relatively high and stable classification accuracies.With parameter changes,RF manifests slightly varying classification accuracies with nonsignificant variation patterns.The results of this study serve as a reference for exploring the object-oriented land cover classification based on moderate-resolution remote sensing data.关键词
面向对象分类/分类器/Landsat/高分一号/土地覆盖Key words
object-oriented classification/classifier/Landsat/GF1/land cover分类
天文与地球科学引用本文复制引用
尚明,马杰,李悦,赵菲,顾鹏程,潘光耀,李倩,任阳阳..Landsat和GF数据面向对象土地覆盖分类研究[J].自然资源遥感,2024,36(3):240-247,8.基金项目
邯郸市科学技术研究与发展计划项目"面向对象的邯郸市土地覆被变化及生态服务功能评价"(编号:21422903273)、新疆干旱区水循环与水利用重点实验室开放课题"干旱区融雪径流模拟研究——以玛纳斯河流域为例"(编号:XJYS0907-2023-11)、国家自然科学基金项目"华北平原农田最大羧化速率和光合色素的关系及其生产力模拟研究"(编号:32001130)和河北省自然科学基金项目"基于叶片光合能力和叶绿素含量同步观测的河北冬小麦-夏玉米生产力模拟研究"(编号:C2021402011)共同资助. （编号:21422903273）
