Change detection of remote sensing images by combining neighborhood information and structural features

In order to improve the accuracy of pixel-level change detection methods, this paper proposes a novel change detection method for remote sensing images by combining neighborhood information (including the neighborhood correlation image (NCI) and matching errors) and structural features. First, a tec...

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Autores principales:	YE Yuanxin, SUN Miaomiao, WANG Mengmeng, TAN Xin
Formato:	article
Lenguaje:	ZH
Publicado:	Surveying and Mapping Press 2021
Materias:	neighborhood information matching errors structural features change detection Mathematical geography. Cartography GA1-1776
Acceso en línea:	https://doaj.org/article/56feba85b33c464ab270a1390e73b772
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id	oai:doaj.org-article:56feba85b33c464ab270a1390e73b772
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spelling	oai:doaj.org-article:56feba85b33c464ab270a1390e73b7722021-11-12T02:25:59ZChange detection of remote sensing images by combining neighborhood information and structural features1001-159510.11947/j.AGCS.2021.20200130https://doaj.org/article/56feba85b33c464ab270a1390e73b7722021-10-01T00:00:00Zhttp://xb.sinomaps.com/article/2021/1001-1595/2021-10-1349.htmhttps://doaj.org/toc/1001-1595In order to improve the accuracy of pixel-level change detection methods, this paper proposes a novel change detection method for remote sensing images by combining neighborhood information (including the neighborhood correlation image (NCI) and matching errors) and structural features. First, a technique of neighborhood correlation analysis is used to obtain the NCI which represents the context information, and the cross-correlation of neighborhood pixels is used to obtain matching errors by a template matching scheme. Then, structure features of images are extracted using orientated gradient information, which are robust to spectral differences between images. Subsequently, the initial change detection results is obtained by using the NCI, the matching errors, and structural features as the classification attributes of a decision tree. Finally, the Markov Random Field (MRF) is used to optimize the results, yielding the final binary map. The proposed method has been evaluated with two sets of bi-temporal remote sensing images from different sensors. Experimental results demonstrate that this method effectively improves the accuracy of change detection compared with the change vector analysis method, the single neighborhood information method and the method combining neighborhood information and texture features.YE YuanxinSUN MiaomiaoWANG MengmengTAN XinSurveying and Mapping Pressarticleneighborhood informationmatching errorsstructural featureschange detectionMathematical geography. CartographyGA1-1776ZHActa Geodaetica et Cartographica Sinica, Vol 50, Iss 10, Pp 1349-1357 (2021)
institution	DOAJ
collection	DOAJ
language	ZH
topic	neighborhood information matching errors structural features change detection Mathematical geography. Cartography GA1-1776
spellingShingle	neighborhood information matching errors structural features change detection Mathematical geography. Cartography GA1-1776 YE Yuanxin SUN Miaomiao WANG Mengmeng TAN Xin Change detection of remote sensing images by combining neighborhood information and structural features
description	In order to improve the accuracy of pixel-level change detection methods, this paper proposes a novel change detection method for remote sensing images by combining neighborhood information (including the neighborhood correlation image (NCI) and matching errors) and structural features. First, a technique of neighborhood correlation analysis is used to obtain the NCI which represents the context information, and the cross-correlation of neighborhood pixels is used to obtain matching errors by a template matching scheme. Then, structure features of images are extracted using orientated gradient information, which are robust to spectral differences between images. Subsequently, the initial change detection results is obtained by using the NCI, the matching errors, and structural features as the classification attributes of a decision tree. Finally, the Markov Random Field (MRF) is used to optimize the results, yielding the final binary map. The proposed method has been evaluated with two sets of bi-temporal remote sensing images from different sensors. Experimental results demonstrate that this method effectively improves the accuracy of change detection compared with the change vector analysis method, the single neighborhood information method and the method combining neighborhood information and texture features.
format	article
author	YE Yuanxin SUN Miaomiao WANG Mengmeng TAN Xin
author_facet	YE Yuanxin SUN Miaomiao WANG Mengmeng TAN Xin
author_sort	YE Yuanxin
title	Change detection of remote sensing images by combining neighborhood information and structural features
title_short	Change detection of remote sensing images by combining neighborhood information and structural features
title_full	Change detection of remote sensing images by combining neighborhood information and structural features
title_fullStr	Change detection of remote sensing images by combining neighborhood information and structural features
title_full_unstemmed	Change detection of remote sensing images by combining neighborhood information and structural features
title_sort	change detection of remote sensing images by combining neighborhood information and structural features
publisher	Surveying and Mapping Press
publishDate	2021
url	https://doaj.org/article/56feba85b33c464ab270a1390e73b772
work_keys_str_mv	AT yeyuanxin changedetectionofremotesensingimagesbycombiningneighborhoodinformationandstructuralfeatures AT sunmiaomiao changedetectionofremotesensingimagesbycombiningneighborhoodinformationandstructuralfeatures AT wangmengmeng changedetectionofremotesensingimagesbycombiningneighborhoodinformationandstructuralfeatures AT tanxin changedetectionofremotesensingimagesbycombiningneighborhoodinformationandstructuralfeatures
_version_	1718431292236759040

Change detection of remote sensing images by combining neighborhood information and structural features

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