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24 October 2019 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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Abstract

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 (Aslantaş 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.

© 2019 Society of Photo-Optical Instrumentation Engineers (SPIE) 1931-3195/2019/$28.00 © 2019 SPIE
Aliihsan Sekertekin "Potential of global thresholding methods for the identification of surface water resources using Sentinel-2 satellite imagery and normalized difference water index," Journal of Applied Remote Sensing 13(4), 044507 (24 October 2019). https://doi.org/10.1117/1.JRS.13.044507
Received: 10 July 2019; Accepted: 24 September 2019; Published: 24 October 2019
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