scholarly journals CHANGE DETECTION METHOD OF MULTIBAND REMOTE SENSING IMAGES BASED ON FAST EXPECTATION MAXIMIZATION ALGORITHM AND FUZZY FUSION

2011 ◽  
Vol 29 (5) ◽  
pp. 383-388 ◽  
Author(s):  
Gui-Ting WANG ◽  
You-Liang WANG ◽  
Li-Cheng JIAO
2020 ◽  
Vol 2020 ◽  
pp. 1-9 ◽  
Author(s):  
Liang Huang ◽  
Qiuzhi Peng ◽  
Xueqin Yu

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.


2016 ◽  
Vol 8 (9) ◽  
pp. 745 ◽  
Author(s):  
Ming Hao ◽  
Wenzhong Shi ◽  
Hua Zhang ◽  
Qunming Wang ◽  
Kazhong Deng

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Liang Huang ◽  
Yuanmin Fang ◽  
Xiaoqing Zuo ◽  
Xueqin Yu

This paper presents a new automatic change detection method of multitemporal remote sensing images based on 2D-Otsu algorithm improved by Firefly algorithm. The proposed method is designed to automatically extract the changing area between two temporal remote sensing images. First, two different temporal remote sensing images were acquired through difference value method of remote sensing images; then, the 2D-Otsu threshold segmentation principles are analyzed and the optimal threshold of 2D-Otsu threshold segmentation method is figured out by using the Firefly algorithm, where the difference images are conducted with binary classification to obtain the changing category and the nonchanging category; finally, the proposed method is used to carry out change detection experiments on the two selected areas, where a variety of methods are compared. Experimental results show that the proposed method can effectively and quickly extract the changing area between the two temporal remote sensing images; thus, it is an effective method of change detection for remote sensing images.


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