Change Detection in Multitemporal High Spatial Resolution Remote-Sensing Images Based on Saliency Detection and Spatial Intuitionistic Fuzzy C-Means Clustering

被引：6

作者：

Huang, Liang ^{[1
,2
]}

Peng, Qiuzhi ^{[1
,2
]}

Yu, Xueqin ^{[3
]}

机构：

[1] Kunming Univ Sci & Technol, Fac Land Resource Engn, Kunming 650093, Yunnan, Peoples R China

[2] Surveying & Mapping Geoinformat Technol Res Ctr P, Kunming 650093, Yunnan, Peoples R China

[3] Kunming Surveying & Mapping Inst, Kunming 650051, Yunnan, Peoples R China

来源：

JOURNAL OF SPECTROSCOPY | 2020年 / 2020卷

基金：

中国国家自然科学基金;

关键词：

UNSUPERVISED CHANGE DETECTION; CHANGE VECTOR ANALYSIS; COVER CHANGE DETECTION; URBAN EXPANSION; MULTISENSOR;

D O I：

10.1155/2020/2725186

中图分类号：

Q5 [生物化学];

学科分类号：

071010 ; 081704 ;

摘要：

In order to improve the change detection accuracy of multitemporal high spatial resolution remote-sensing (HSRRS) images, a change detection method of multitemporal remote-sensing images based on saliency detection and spatial intuitionistic fuzzy C-means (SIFCM) clustering is proposed. Firstly, the cluster-based saliency cue method is used to obtain the saliency maps of two temporal remote-sensing images; then, the saliency difference is obtained by subtracting the saliency maps of two temporal remote-sensing images; finally, the SIFCM clustering algorithm is used to classify the saliency difference image to obtain the change regions and unchange regions. Two data sets of multitemporal high spatial resolution remote-sensing images are selected as the experimental data. The detection accuracy of the proposed method is 96.17% and 97.89%. The results show that the proposed method is a feasible and better performance multitemporal remote-sensing image change detection method.

引用

页数：9

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