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Article

Extraction of Yardang Characteristics Using Object-Based Image Analysis and Canny Edge Detection Methods

1
School of Earth Sciences, China University of Geosciences, Wuhan 430074, China
2
Guangdong Provincial Key Laboratory of Marine Biotechnology, Institute of Marine Sciences, Shantou University, Shantou 515063, China
3
College of Geology Engineering and Geomatics, Chang’an University, Xi’an 710054, China
4
School of Geodesy and Geomatics, Wuhan University, Wuhan 430074, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(4), 726; https://doi.org/10.3390/rs12040726
Received: 31 December 2019 / Revised: 18 February 2020 / Accepted: 20 February 2020 / Published: 22 February 2020
(This article belongs to the Special Issue Remote Sensing of Dryland Environment)
Parameters of geomorphological characteristics are critical for research on yardangs. However, methods which are low-cost, accurate, and automatic or semi-automatic for extracting these parameters are limited. We present here semi-automatic techniques for this purpose. They are object-based image analysis (OBIA) and Canny edge detection (CED), using free, very high spatial resolution images from Google Earth. We chose yardang fields in Dunhuang of west China to test the methods. Our results showed that the extractions registered an overall accuracy of 92.26% with a Kappa coefficient of agreement of 0.82 at a segmentation scale of 52 using the OBIA method, and the exaction of yardangs had the highest accuracy at medium segmentation scales (138, 145). Using CED, we resampled the experimental image subset to a series of lower spatial resolutions for eliminating noise. The total length of yardang boundaries showed a logarithmically decreasing (R2 = 0.904) trend with decreasing spatial resolution, and there was also a linear relationship between yardang median widths and spatial resolutions (R2 = 0.95). Despite the difficulty of identifying shadows, the CED method achieved an overall accuracy of 89.23% with a kappa coefficient of agreement of 0.72, similar to that of the OBIA method at medium segmentation scale (138). View Full-Text
Keywords: extracting yardang geomorphological characteristics; object-based image analysis (OBIA); Canny edge detection (CED); free very high-resolution image in Google Earth extracting yardang geomorphological characteristics; object-based image analysis (OBIA); Canny edge detection (CED); free very high-resolution image in Google Earth
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MDPI and ACS Style

Yuan, W.; Zhang, W.; Lai, Z.; Zhang, J. Extraction of Yardang Characteristics Using Object-Based Image Analysis and Canny Edge Detection Methods. Remote Sens. 2020, 12, 726. https://doi.org/10.3390/rs12040726

AMA Style

Yuan W, Zhang W, Lai Z, Zhang J. Extraction of Yardang Characteristics Using Object-Based Image Analysis and Canny Edge Detection Methods. Remote Sensing. 2020; 12(4):726. https://doi.org/10.3390/rs12040726

Chicago/Turabian Style

Yuan, Weitao, Wangle Zhang, Zhongping Lai, and Jingxiong Zhang. 2020. "Extraction of Yardang Characteristics Using Object-Based Image Analysis and Canny Edge Detection Methods" Remote Sensing 12, no. 4: 726. https://doi.org/10.3390/rs12040726

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