多特征多层次Sentinel-2影像辽宁省湖库水体提取

Multi-feature and multi-level Sentinel-2 image extraction of lake and reservoir water bodies in Liaoning province

以辽宁省为研究区,本文基于GEE遥感云平台,使用Sentinel-2遥感影像,提出了一种多特征多层次的湖库水体提取算法。该算法选择自动水体指数(AWEI sh)和改进的归一化水体指数(MNDWI)提取水体,并利用归一化植被指数(NDVI)、归一化建筑指数(NDBI)、归一化差异红边指数(NDREI)、Sentinel-2的B8和B9波段及DEM数据多层次地消除暗地物和高亮地物噪声,对提取结果中被云雾遮挡而部分缺失的水体进行修复,最后将河流及细小像素剔除。利用此算法提取了辽宁省2017—2021年每年4、7、10月的湖库水体,并对比了不同水体提取算法及不同的水体数据产品。试验结果表明,本文算法在大尺度条件下提取水体具有良好的效果,总体精度达96%以上,可以较好地去除植被、阴影等暗像元表面,并且保证了水体信息的完整性,在大尺度水体提取方面具有一定的适用性和稳定性。

This article takes Liaoning province as the research area,based on the GEE(Google Earth Engine)remote sensing cloud platform,and using Sentinel-2 remote sensing images,proposes a multi-feature and multi-level algorithm for extracting lake and reservoir water bodies.This algorithm selects the automatic water index(AWEI sh)and the improved normalized water index(MNDWI)to extract water bodies,and uses the normalized vegetation index(NDVI),normalized building index(NDBI),normalized difference red edge index(NDREI),Sentinel-2 s B8 and B9 bands,as well as DEM data to multi-level eliminate dark and bright ground noise,and to repair partially missing water bodies in the extraction results that are obscured by clouds and mist.Finally,remove the river and small pixels.This algorithm is used to extract lake and reservoir water bodies in Liaoning province from April,July,and October of each year from 2017 to 2021.Different water body extraction algorithms and water body data products were compared.The experimental results showed that the proposed algorithm had good performance in extracting water bodies under large-scale conditions,with an overall accuracy of over 96%.It can effectively remove dark pixel surfaces such as vegetation and shadows,and ensure the integrity of water body information,It has certain applicability and stability in large-scale water extraction.