2017 9th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp)

2017 ◽  
Keyword(s):  
Remote Sensing ◽  
International Workshop ◽  
Remote Sensing Images ◽  
Multitemporal Remote Sensing

Cloud masking of multitemporal remote sensing images

2014 ◽  
Author(s):  
L. Gómez-Chova ◽  
J. Amorós-López ◽  
J. Muñoz-Marí ◽  
G. Camps-Valls
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Images ◽  
Multitemporal Remote Sensing

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

2020 ◽  
Vol 2020 ◽  
pp. 1-9 ◽  
Author(s):  
Liang Huang ◽  
Qiuzhi Peng ◽  
Xueqin Yu
Keyword(s):  
Remote Sensing ◽  
Change Detection ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Detection Method ◽  
Saliency Detection ◽  
Detection Accuracy ◽  
Remote Sensing Images ◽  
Saliency Maps ◽  
Multitemporal Remote Sensing

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.

Sign in / Sign up

Export Citation Format

Share Document