Sentinel-2和AW3D30相结合的湿地提取  

Wetland extraction method combined with Sentinel-2 and AW3D30 data

作　　者：陈光 卜坤[1] CHEN Guang;BU Kun(Northeast Institute of Geography and Agroecology,Chinese Academy of Sciences,Changchun 130102,China)

机构地区：[1]中国科学院东北地理与农业生态研究所,吉林长春130102

出　　处：《测绘通报》2020年第11期19-22,共4页Bulletin of Surveying and Mapping

基　　金：中国科学院信息化专项课题(XXH-13514-0306)。

摘　　要：为了实现大范围湿地动态监测,本文以辽河入海口附近的盘锦湿地为研究区,基于Sentinel2-L1C和AW3D30 DSM数据,在随机森林分类的基础上,结合地形数字特征和多边形形状特征对研究区进行湿地信息提取。通过人工目视解译对该分类方法进行精度验证,结果表明:该方法的自动化程度较高,能够在较少的人工干预下提取湿地覆盖范围。提取结果精度较高,制图精度和总体精度分别为91.04%和82.65%,Kappa系数为0.599 7,说明本文所采用的计算机分类方法与人工目视解译方法具有较好的一致性。This paper mainly studies the wetland cover in Liao River estuary.In order to realize dynamic monitoring of large-scale wetland,the Sentinel2-L1C and AW3D30 DSM have been used as basic data and the random forest model has been used for raster classification.The results extracted by random forest model will be further processed by using digital signature of terrain and polygonal shape characteristics.The visual interpretation method has been used for verifying the accuracy of the classification,the results showed:this method has a high degree of automation,which can extract wetland coverage with less manual intervention.The extraction results achieved higher precision with the producer’s and overall accuracy of 91.04% and 82.65%,respectively.The Kappa coefficient is 0.599 7,which indicated that the computer classification method this paper used and artificial visual interpretation have good consistency.

关 键 词：盘锦湿地 随机森林 信息提取 遥感分类 目视解译 

分 类 号：P237[天文地球—摄影测量与遥感]