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Open AccessArticle

Change Detection of Water Resources via Remote Sensing: An L-V-NSCT Approach

School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha 410114, Hunan, China
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Appl. Sci. 2019, 9(6), 1223; https://doi.org/10.3390/app9061223
Received: 19 February 2019 / Revised: 19 March 2019 / Accepted: 19 March 2019 / Published: 22 March 2019
(This article belongs to the Special Issue Intelligent Processing on Image and Optical Information)
Aiming at the change detection of water resources via remote sensing, the non-subsampling contour transformation method combining a log-vari model and the Stractural Similarity of Variogram (VSSIM) model, namely log-vari and VSSIM based non-subsampled contourlet transform (L-V-NSCT) approach, is proposed. Firstly, a differential image construction method based on non-subsampled contourlet transform (NSCT) texture analysis is designed to extract the low-frequency and high-frequency texture features of the objects in the images. Secondly, the texture features of rivers, lakes and other objects in the images are accurately classified. Finally, the change detection results of regions of interest are extracted and evaluated. In this experiment, the L-V-NSCT approach is compared with other methods with the results showing the effectiveness of this method. The change in Dongting Lake is also analyzed, which can be used as a reference for relevant administrative departments. View Full-Text
Keywords: change detection; NSCT; variogram function; structure similarity; Dongting Lake change detection; NSCT; variogram function; structure similarity; Dongting Lake
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MDPI and ACS Style

Xin, W.; Can, T.; Wei, W.; Ji, L. Change Detection of Water Resources via Remote Sensing: An L-V-NSCT Approach. Appl. Sci. 2019, 9, 1223.

AMA Style

Xin W, Can T, Wei W, Ji L. Change Detection of Water Resources via Remote Sensing: An L-V-NSCT Approach. Applied Sciences. 2019; 9(6):1223.

Chicago/Turabian Style

Xin, Wang; Can, Tang; Wei, Wang; Ji, Li. 2019. "Change Detection of Water Resources via Remote Sensing: An L-V-NSCT Approach" Appl. Sci. 9, no. 6: 1223.

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