首页> 外文期刊>Journal of Applied Remote Sensing >Potential of global thresholding methods for the identification of surface water resources using Sentinel-2 satellite imagery and normalized difference water index
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Potential of global thresholding methods for the identification of surface water resources using Sentinel-2 satellite imagery and normalized difference water index

机译:使用哨兵-2卫星图像识别地表水资源的全局阈值方法的潜力和归一化差异水指数

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摘要

The aim of this study is to investigate the performance of 15 automatic thresholding methods, namely Huang and Wang's fuzzy thresholding method, intermode thresholding method, isodata thresholding method, Li and Tam's thresholding method, maximum entropy thresholding method, mean thresholding method, minimum error thresholding method, minimum thresholding method, moment-preserving thresholding method, Otsu' s thresholding method, percentile (p-tile) thresholding method, Renyi' s entropy thresholding method, Shanbhag's thresholding method, triangle thresholding method, and Yen's thresholding method, for mapping water body using Sentinel-2 data based on normalized difference water index. Three different types of surface water bodies, such as a natural lake (Lake Burdur), a dam reservoir (Aslantas Dam Reservoir), and a part of a river (Aras River), are chosen to reveal the potential of 15 thresholding methods. The reference water body maps of each test site were generated by manual digitization of high-resolution Google Earth images. The thresholding methods were assessed using the statistical measures, namely overall accuracy (OA), Kappa coefficient, producer's accuracy, user's accuracy, and misclassification error (ME). The accuracy analyses of 15 thresholding methods were carried out separately for each test site, and then the overall accuracies were calculated to determine the best method. The obtained OA results showed that minimum thresholding method was the best method among these 15 algorithms with 0.0008 ME, 99.92% OA, and 0.9758 Kappa coefficient. On the other hand, Shanbhag' s method provided the lowest overall accuracies as 0.3133 ME, 68.67% OA, and 0.3190 Kappa coefficient. (C) 2019 Society of Photo-Optical Instrumentation Engineers (SPIE)
机译:本研究的目的是研究15个自动阈值的性能,即黄和王的模糊阈值方法,相反阈值方法,ISODATA阈值方法,LI和TAM的阈值方法,最大熵阈值方法,均值阈值阈值方法,最小误差阈值阈值方法,最小阈值方法,时刻保留阈值方法,OTSU的阈值方法,百分位数(P-TILE)阈值方法,renyi的熵阈值方法,Shanbhag的阈值方法,三角形阈值方法和Yen的阈值方法,用于映射水正文使用基于归一化差异水指数的Sentinel-2数据。选择了三种不同类型的地表水体,如天然湖(湖泊布尔德尔),坝水库(Aslantas Dam Chockoir)和河流(Aras River)的一部分,以揭示15种门槛的潜力。每个测试站点的参考水体图是通过高分辨率Google接地图像的手动数字化生成的。使用统计措施,即总体精度(OA),Kappa系数,生产者的准确性,用户的准确性和错误分类错误(ME)评估阈值方法。对于每个测试部位分别进行15个阈值化方法的精度分析,然后计算整体精度以确定最佳方法。所获得的OA结果表明,最小阈值化方法是这15种算法中的最佳方法,具有0.0008 Me,99.92%OA和0.9758 kappa系数。另一方面,Shanbhag的方法提供了0.3133 ME,68.67%OA和0.3190 kappa系数的最低总精度。 (c)2019年光学仪表工程师协会(SPIE)

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