基于PUL算法及高分辨率WorldView影像的城市不透水面提取

Urban Impervious Surface Mapping from High Resolution WorldView Imagery Based on PUL Algorithm

准确提取城市不透水面对生态环境、水热循环及热岛效应等研究具有重要意义。该文利用WorldView高分辨遥感影像,提出基于PUL(Positive and Unlabeled Learning)算法的高分辨率影像城市不透水面提取方法,该方法不需要负样本数据,只需少量的正样本和未标记样本即可训练分类模型。结果显示,PUL算法的提取结果优于一类支持向量机(OCSVM)以及最大熵(MAXENT)模型。使用不同正样本量时,PUL的提取结果总体精度和kappa系数均优于OCSVM和MAXENT,最高总体精度为91.27%,最高kappa系数可达0.8255,可快速、有效地从高分辨率遥感影像中提取不透水面。

Correctly mapping the urban impervious surfaces is important in the studies of urban environment,hydrothermal cycle and the heat island effect.In this study,the positive and unlabeled learning(PUL)algorithm was investigated,which trained a classifier on positive and unlabeled data,to map the urban impervious surfaces based on the high-resolution WorldView images.Different from traditional supervised classification methods,which require both labeled positive and negative training data,the PUL algorithm requires only labeled positive and unlabeled training data.Experimental results show that the PUL algorithm outperforms the One-Class Support Vector Machine(OCSVM)and the Maximum Entropy(MAXENT)methods.For all training sample sizes,PUL consistently produces higher accuracies(i.e.overall accuracy and kappa coefficient)than the other two methods.The highest overall accuracy and kappa coefficient obtained by PUL are 91.27% and 0.8255,respectively.Thus,PUL is an efficient and promising method for extracting impervious surfaces from high-resolution remote sensing images.