融合随机森林模型和6种水体指数的上海市水体信息提取  

Water information extraction in Shanghai by integrating random forest model and six water indices

作　　者：崔青林 汪鸣泉[1,3] 黄永健 CUI Qinglin;WANG Mingquan;HUANG Yongjian(Shanghai Carbon Data Research Center,Shanghai Advanced Research Institute,Chinese Academy of Sciences,Shanghai 201210,China;University of Chinese Academy of Sciences,Beijing 100049,China;Key Laboratory of Low-carbon Conversion Science and Engineering,Chinese Academy of Sciences,Shanghai 201210,China)

机构地区：[1]中国科学院上海高等研究院上海碳数据与碳评估中心,上海201210 [2]中国科学院大学,北京100049 [3]中国科学院低碳转化科学与工程重点实验室,上海201210

出　　处：《测绘通报》2022年第2期106-109,共4页Bulletin of Surveying and Mapping

基　　金：上海市科技创新行动计划社会发展科技攻关项目(20dz1204302);国家自然科学基金(51778601)。

摘　　要：为快速、准确地掌握水体分布信息,本文以上海市为研究区,基于多时相Sentinel-2卫星数据构建水体提取特征集,并采用效率高、稳健性好的随机森林模型,对研究区内的水体进行提取。水体提取特征集在现有光谱波段特征的基础上加入6种水体指数,分别为NDWI、MNDWI、AWEI;、WI;、SWI和RWI,旨在提高水体提取精度。针对10个光谱波段特征及6种水体指数,设计了8种试验方案探究加入水体指数对于水体提取的作用。结果表明,将6种水体指数全部加入的方案精度最高,为97.910%;NDWI和RWI能提高水体提取精度、降低漏提率和误提率。In order to know water distribution information quickly and accurately, this paper selects Shanghai as the study area, constructs a feature set of water extraction based on multi-temporal Sentinel-2 satellite data, uses random forest model with high efficiency and good robustness to extract water in Shanghai. To improve the accuracy of water extraction, six water indices are added into the feature set of water extraction based on the characteristics of existing spectral bands: NDWI、MNDWI、AWEI;、WI;、SWI and RWI. According to the characteristics of 10 spectral bands and 6 water indices, eight experimental schemes are designed to explore the effect of adding water indexes on water extraction. The results show that the scheme which included all the six water indices has the highest overall accuracy, which is 97.910%. NDWI and RWI can also improve the accuracy of water extraction and reduce the rate of leakage and error.

关 键 词：水体信息提取 随机森林 Sentinel-2 水体指数 上海市 

分 类 号：P237[天文地球—摄影测量与遥感]