基于多源遥感的大尺度高分辨率不透水面深度学习提取方法

A method for large-scale and high-resolution impervious surface extraction based on multi-source remote sensing and deep learning

深度学习是提取不透水面的一类重要方法,具有精度高,泛化性强等优势。但是模型的训练需要依靠大量的训练样本。尤其是在高分辨率、大尺度不透水面制图时,获取数量足够且高质量的训练样本非常费时费力。因此,本文结合多源遥感影像与开源数据,提出了一种大尺度高分辨率不透水面自动提取方法。该方法首先从众源数据OpenStreetMap中自动获取训练样本,然后用开源的不透水面产品对噪声样本加权,减小标签噪声对模型训练的负面影响;在此基础上,构建了一种三分支的超轻量级CNN模型,融合光学、SAR和地形数据生成10 m不透水面产品。以越南全境为试验区,对本文方法进行了验证。试验结果表明,本文提出的方法分类总体精度和Kappa系数分别为91.01%和0.82,优于目前已发布的不透水面产品。本文研究成果可为澜湄流域等热带亚热带城市可持续发展和生态环境保护提供基础技术和数据支撑。

Deep learning is an important method for extracting impervious surfaces(IS),which has the advantages of high accuracy and generalization.However,the training of the models relies on a huge of train samples.Especially in large-scale and high-resolution IS mapping,it is time-consuming and laborious to obtain sufficient and high-quality training samples.Therefore,this study proposes a method to automatically extract IS based on multi-source remote sensing images and open-source data.Firstly,training samples are automatically obtained from crowdsourced OpenStreetMap data,and then the noise samples are weighted with open-source IS maps to reduce the negative influence of label noise on model training.Moreover,an ultra-lightweight CNN model with three branches was constructed to generate 10 m IS products by integrating optical,SAR and terrain data.In this paper,the method was validated in Vietnam.The results show that the overall accuracy and Kappa coefficient of the method proposed are 91.01%and 0.82,respectively,which are better than the currently released IS products.The research results of this paper can provide basic technology and data support for the sustainable development and ecological environment protection of tropical and subtropical cities in the Lancing-Mekong River basin.