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

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

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 pro...

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Main Authors: Wang Xin, Tang Can, Wang Wei, Li Ji
Format: Article
Language:English
Published: MDPI AG 2019-03-01
Series:Applied Sciences
Subjects:
change detection
NSCT
variogram function
structure similarity
Dongting Lake
Online Access:https://www.mdpi.com/2076-3417/9/6/1223
id doaj-9b306acaf4454aa497d3b394683d9239
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spelling doaj-9b306acaf4454aa497d3b394683d92392020-11-24T21:49:07ZengMDPI AGApplied Sciences2076-34172019-03-0196122310.3390/app9061223app9061223Change Detection of Water Resources via Remote Sensing: An L-V-NSCT ApproachWang Xin0Tang Can1Wang Wei2Li Ji3School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha 410114, Hunan, ChinaSchool of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha 410114, Hunan, ChinaSchool of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha 410114, Hunan, ChinaSchool of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha 410114, Hunan, ChinaAiming 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.https://www.mdpi.com/2076-3417/9/6/1223change detectionNSCTvariogram functionstructure similarityDongting Lake
collection DOAJ
language English
format Article
sources DOAJ
author Wang Xin
Tang Can
Wang Wei
Li Ji
spellingShingle Wang Xin
Tang Can
Wang Wei
Li Ji
Change Detection of Water Resources via Remote Sensing: An L-V-NSCT Approach
Applied Sciences
change detection
NSCT
variogram function
structure similarity
Dongting Lake
author_facet Wang Xin
Tang Can
Wang Wei
Li Ji
author_sort Wang Xin
title Change Detection of Water Resources via Remote Sensing: An L-V-NSCT Approach
title_short Change Detection of Water Resources via Remote Sensing: An L-V-NSCT Approach
title_full Change Detection of Water Resources via Remote Sensing: An L-V-NSCT Approach
title_fullStr Change Detection of Water Resources via Remote Sensing: An L-V-NSCT Approach
title_full_unstemmed Change Detection of Water Resources via Remote Sensing: An L-V-NSCT Approach
title_sort change detection of water resources via remote sensing: an l-v-nsct approach
publisher MDPI AG
series Applied Sciences
issn 2076-3417
publishDate 2019-03-01
description 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.
topic change detection
NSCT
variogram function
structure similarity
Dongting Lake
url https://www.mdpi.com/2076-3417/9/6/1223
work_keys_str_mv AT wangxin changedetectionofwaterresourcesviaremotesensinganlvnsctapproach
AT tangcan changedetectionofwaterresourcesviaremotesensinganlvnsctapproach
AT wangwei changedetectionofwaterresourcesviaremotesensinganlvnsctapproach
AT liji changedetectionofwaterresourcesviaremotesensinganlvnsctapproach
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