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
收藏
页数：9
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