A simple automated dynamic threshold extraction method for the classification of large water bodies from landsat-8 OLI water index images

ABSTRACT

Traditional manual methods of extracting water bodies from remote sensing images cannot satisfy the requirements for mass processing of remote sensing data, and new automated methods are complicated and require a large amount of auxiliary data. The histogram bimodal method is a frequently used objective tool for threshold selection in image segmentation. However, automatically calculating the threshold is difficult because of complex surfaces and image noise, which lead to imperfect twin peaks. To overcome these difficulties, we developed an operational automated water extraction method. This method does not require the identification of twin histogram peaks but instead seeks minimum values in the threshold range to achieve an automated dynamic threshold. We calibrated the method for 18 lakes in China using Landsat 8 Operational Land Imager images, for which the relative error (RE) and coefficient of determination (R²) for threshold accuracy were 2.1% and 0.96, respectively. The RE of area accuracy was 0.59%. The advantages of the method lie in its simplicity and minimal requirements for auxiliary data while still achieving an accuracy comparable to that of other automatic water extraction methods. It can be applied to mass remote sensing data to calculate water thresholds and automatically extract large water bodies.

Acknowledgments

We would like to thank Yongxue Liu and Yuhao Yang for providing the MMFCM program, the Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences and Geospatial Data Cloud for providing Landsat images, the Joint Research Centre for providing Global Surface Water (GSW) data, and the Global Land Cover Facility for providing Global Inland Water products (GIW).

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the Director Foundation of the Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences under Grant number Y6SJ2100CX, High Resolution Earth Observation Systems of National Science and Technology Major Projects under Grant number 41-Y20A31-9003-15/17, Strategic Priority Research Program of the Chinese Academy of Sciences, Grant number XDA19080304, National Natural Science Foundation of China (41701402, 41471308, 41571361, 91638201), and Youth Innovation Promotion Association of Chinese Academy of Sciences (2015128)