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Remote Sensing for Natural Resources    2023, Vol. 35 Issue (4) : 96-104     DOI: 10.6046/zrzyyg.2022260
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Noise-resistant change detection for remote sensing images based on spatial fuzzy C-means clustering and a Bayesian network
WANG Zihao1(), LI Yikun1,2,3(), LI Xiaojun1,2,3, YANG Shuwen1,2,3
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
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Abstract  

Currently, most change detection algorithms for remote sensing images fail to effectively process images polluted by Gaussian, impulse, or mixed noise. To address this problem, this study presented five fuzzy C-means (FCM) clustering algorithms (FCM_S1, FCM_S2, KFCM_S1, KFCM_S2, and FLICM) based on neighborhood space information. These algorithms, which can efficiently decompose mixed pixels in the presence of noise pollution, were combined with a simple Bayesian network (SBN). Under the framework of change vector analysis in posterior probability space (CVAPS), this study developed five change detection methods for remote sensing images, exhibiting high resistance to Gaussian, impulse, and mixed noise. Comparative experiments demonstrate that the change detection algorithms proposed in this study manifest high robustness against the above-mentioned noise.
Keywords change detection      fuzzy C-means clustering      simple Bayesian network      change vector analysis in posterior probability space     
ZTFLH:  TP79  
Issue Date: 21 December 2023
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Zihao WANG
Yikun LI
Xiaojun LI
Shuwen YANG
Cite this article:   
Zihao WANG,Yikun LI,Xiaojun LI, et al. Noise-resistant change detection for remote sensing images based on spatial fuzzy C-means clustering and a Bayesian network[J]. Remote Sensing for Natural Resources, 2023, 35(4): 96-104.
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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2022260     OR     https://www.gtzyyg.com/EN/Y2023/V35/I4/96
Fig.1  Simple Bayesian network
Fig.2  Algorithm flowchart
Fig.3  Experimental images and manual change detection result
Fig.4  Change effect images(5% salt and pepper noise)
指标 FCM-SBN-
CVAPS		 SVM-
CVAPS		 FCM_S2-
SBN-
CVAPS		 KFCM_S2-
SBN-CVAPS
错检率 0.341 0.684 0.209 0.325
漏检率 0.202 0.070 0.076 0.095
总体精度 0.953 0.841 0.975 0.959
Kappa系数 0.647 0.406 0.844 0.751
Tab.1  Change detection model performance comparison(5% salt and pepper noise)
Fig.5  Change detection effect images(Gaussian noise with zero mean, variance 0.01)
指标 FCM-SBN-
CVAPS		 SVM-
CVAPS		 FCM_S1-
SBN-
CVAPS		 KFCM_S1-
SBN-CVAPS
错检率 0.501 0.594 0.199 0.070
漏检率 0.525 0.291 0.131 0.241
总体精度 0.923 0.898 0.973 0.977
Kappa系数 0.497 0.466 0.798 0.830
Tab.2  Change detection model performance comparison (Gaussian noise with zero mean, variance 0.01)
Fig.6  Change detection effect images(0.5% salt and pepper noise + zero mean, gaussian noise with variance 0.001)
指标 FCM-SBN-
CVAPS		 SVM-
CVAPS		 FLICM-
SBN-CVAPS
错检率 0.347 0.594 0.071
漏检率 0.006 0.291 0.187
总体精度 0.957 0.898 0.979
Kappa系数 0.751 0.549 0.851
Tab.3  Change detection model performance comparison (0.5% salt and pepper noise + zero mean, gaussian noise with variance 0.001)
Fig.7  Noise sensitivity diagrams of SVM-CVAPS, FCM-SBN-CVAPS algorithms and four improved algorithms
算法 椒盐噪
声0.4%		 椒盐噪
声0.6%		 零均值,方差为
0.001高斯噪声		 零均值,方差为0.001高
斯噪声+椒盐噪声0.2%		 零均值,方差为0.001高
斯噪声+椒盐噪声0.4%		 零均值,方差为0.001高
斯噪声+椒盐噪声0.6%
FLICM-SBN-CVAPS 0.868 0.720 0.853 0.861 0.778 0.754
FCM-SBN-CVAPS 0.821 0.700 0.501 0.767 0.742 0.728
FCM_S1-SBN-CVAPS 0.885 0.882 0.873 0.864 0.876 0.861
FCM_S2-SBN-CVAPS 0.882 0.881 0.881 0.881 0.883 0.880
SVM-CVAPS 0.693 0.668 0.596 0.675 0.638 0.538
Tab.4  Noise sensitivity table for FLICM-SBN-CVAPS(Kappa coefficient value)
Fig.8  Fuzzy degree sensitivity diagram
Fig.9  Sensitivity diagram for the number of training samples
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