Abstract
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.
Highlights
Remote-sensing image change detection is the main mean to identify and monitor the information on land-covered change caused by human activities and natural processes
For two sets of images, the proposed method has the highest overall accuracy (OA), Kappa, and F-measure values, which are significantly better than the other three methods. e IRSIFCM is only lower than the proposed method of the OA evaluation index, but there is a serious missed alarms (MAs). e MA for the second group of images is as high as 98.34%, and the Kappa and F-measure indicators of the second group of images are lowest; the results of univariate image differencing (UID)-spatial intuitionistic fuzzy C-means (SIFCM) and change vector analysis (CVA)-SIFCM are close to each other in the three evaluation indexes, and the Accuracy evaluation of the first comparative experiment of the first group of images
From the visual comparison and quantitative evaluation, we can see that compared with the difference images constructed by the traditional UID, IR and CVA, the difference image constructed by the salience difference image (SDI) has the highest quality. e experimental results verify the feasibility and reliability of the proposed method
Summary
Remote-sensing image change detection is the main mean to identify and monitor the information on land-covered change caused by human activities and natural processes. E main steps of the direct comparison method include difference image construction, binary classification, analysis, and evaluation of detection results. E algebraic operation methods include univariate image differencing (UID) [9], vegetation index differencing (VID) [9], image rationing/log rationing (IR/ LR) [9, 10], mixed method [11, 12], regression [9], and change vector analysis (CVA) [9]. Huang et al got the difference image of two temporal remote-sensing images by UID, IR, and mixed method, and the 2D-OTSU method improved by the firefly algorithm was used to segment the difference image to obtain the change areas [13]; in order to solve the impact of speckle noise on the change detection accuracy of multitemporal SAR images, Gao et al proposed a modified-logratio (MLR) operator for change detection of targets in forest concealment [14]. There are some problems with the clustering algorithm represented by traditional FCM, such as the objective function is easy to fall into local minimum, the function convergence speed is slow, and it is sensitive to initial value and noise, which affects the accuracy of change detection
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