A study of selection method of NDWI segmentation threshold for GF-1 image

doi:10.6046/gtzyyg.2019.01.13

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Abstract

Based on 15 lakes located in different regions of China, the authors calculated the normalized difference water index (NDWI) for GF-1 satellite remote sensing images, and then employed iterative method, Otsu method and histogram bimodal method for segmentation threshold selection and water information extraction, and finally analyzed the threshold selection results and water information extraction results of the three methods. According to the results obtained, iterative method is similar to the threshold chosen by Otsu method, and the difference between the thresholds selected by histogram bimodal method is large; the iterative method is more efficient; the extraction accuracy of bimodal method is the highest, and its fitting effect is the best. This study can provide selection strategies of adaptive threshold segmentation method for extracting accurate water information from GF-1 images.

Keywords GF-1 data NDWI adaptive threshold water extraction

TP79

Corresponding Authors: Lingkui MENG E-mail: Lkmeng@whu.edu.cn

Issue Date: 15 March 2019

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	Yilin JIA
	Wen ZHANG
	Lingkui MENG

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Yilin JIA,Wen ZHANG,Lingkui MENG. A study of selection method of NDWI segmentation threshold for GF-1 image[J]. Remote Sensing for Land & Resources, 2019, 31(1): 95-100.

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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2019.01.13 OR https://www.gtzyyg.com/EN/Y2019/V31/I1/95

Tab.1 Distribution of test lakes

Tab.2 Thresholds of lake images obtained by different methods

Fig.1 Relationship between the image thresholds of lakes obtained by different methods

Tab.3 Time of different methods in threshold selection process(s)

Tab.4 Confusion matrix of results extracted by different methods

Tab.5 Extracting precision of different methods(%)

Tab.6 Statistics of absolute differences between areas extracted by different methods and reference areas(km²)

Fig.2 Relationship between areas extracted by different methods and reference areas

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