A change vector analysis in posterior probability space combined with fuzzy C-means clustering and a Bayesian network
LI Yikun1,2(), YANG Yang1, YANG Shuwen1,2,3, WANG Zihao1
1. Faculty of Geomatics,Lanzhou Jiaotong University, Lanzhou 730070, China 2. National-Local Joint Engineering Research Center of Technologies and Applications for National Geographic State Monitoring, Lanzhou 730070, China 3. Gansu Provincial Engineering Laboratory for National Geographic State Monitoring, Lanzhou 730070, China
The change vector analysis in posterior probability space (CVAPS) method has been widely used in the change detection of remote sensing images owing to its many advantages. It uses the support vector machine (SVM) to estimate the posterior probability vector. However, in the classification of low and medium resolution remote sensing images, SVM cannot effectively deal with the problems of the same object with the different spectra, different objects with the same spectrum, and mixed pixels and thus cannot guarantee the accuracy of the final detection results. Therefore, this paper adopts the fuzzy c-means (FCM) clustering for modeling and couples the FCM with a simple Bayesian network (SBN) to solve the problem of mixed pixels and estimate the posterior probability vector, thus achieving a new posterior probability space change vector analysis method. The experimental results indicate that, compared to the SVM-based CVAPS algorithm, the algorithm proposed in this study shows higher overall accuracy, higher Kappa coefficient, more reliable performance that is less affected by the number of training samples, simpler parameter setting, and lower time consumption. Therefore, the algorithm proposed in this paper helps to improve the accuracy and efficiency of the change detection of remote sensing images.
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LI Yikun, YANG Yang, YANG Shuwen, WANG Zihao. A change vector analysis in posterior probability space combined with fuzzy C-means clustering and a Bayesian network. Remote Sensing for Natural Resources, 2021, 33(4): 82-88.
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2021, Vol. 33 
), 杨洋1, 杨树文1,2,3, 王子浩1
