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

针对中分辨率遥感数据面向对象分类,以河北省北部山区和南部平原Landsat8 OLI,Landsat5 TM及高分一号(GF1)数据为研究对象,对支持向量机(support vector machine,SVM)、随机森林(random forest,RF)、决策树(decision tree,DT)及朴素贝叶斯(naive Bayes,NB)4种分类器的土地覆盖分类效果进行对比,并分析SVM,RF和DT中关键参数对分类结果的影响。结果表明:在2个研究区,各分类器结果略有差异,从整体上看其优劣排序为SVM,NB,RF和DT。其中SVM和DT分类精度随参数变化波动较大:对于SVM,当参数C取值不小于103且gamma不大于10^(-1)时,无论哪种情况其分类精度均优于90%;对于DT,当参数树深(Depth)大于3时,各情况下的分类精度相对较高且趋于稳定。RF分类精度随参数变化波动较小且没有明显的变化规律。研究结果可为中分辨率遥感数据面向对象土地覆盖分类研究提供参考。

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.