基于空间模糊C均值聚类和贝叶斯网络的抗噪声遥感图像变化检测

doi:10.6046/zrzyyg.2022260

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摘要

目前,大部分遥感变化检测算法无法有效处理受高斯、椒盐和混合噪声污染的图像。为了解决这一问题,文章将能够在噪声污染条件下有效分解混合像元的5种基于邻域空间信息的模糊C均值聚类(FCM_S1,FCM_S2,KFCM_S1,KFCM_S2和FLICM)算法分别与简单贝叶斯网络(simple Bayesian network,SBN)相结合,在后验概率空间变化向量分析(change vector analysis in posterior probability space,CVAPS) 框架下,实现了5种能够较好地抗高斯、椒盐和混合噪声的遥感变化检测方法。对比实验证明,该文所提出的变化检测算法对高斯、椒盐和混合噪声具有较好的鲁棒性。

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	王子浩
	李轶鲲
	李小军
	杨树文

关键词 ：变化检测, 模糊C均值聚类, 简单贝叶斯网络, 后验概率空间变化向量分析

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.

Key words： change detection fuzzy C-means clustering simple Bayesian network change vector analysis in posterior probability space

收稿日期: 2022-06-22 出版日期: 2023-12-21

ZTFLH:

TP79

基金资助:国家自然科学基金项目“基于脉冲耦合神经网络的高光谱遥感图像融合方法研究”(41861055);中国博士后基金项目(2019M653795);兰州交通大学优秀平台(201806)

通讯作者: 李轶鲲(1978-),男,博士,副教授,主要从事图像处理方面的研究。Email: liyikun2003@hotmail.com。

作者简介: 王子浩(1998-),男,硕士研究生,研究方向为遥感图像变化检测。Email: 1344744067@qq.com。

引用本文:

王子浩, 李轶鲲, 李小军, 杨树文. 基于空间模糊C均值聚类和贝叶斯网络的抗噪声遥感图像变化检测[J]. 自然资源遥感, 2023, 35(4): 96-104.
WANG Zihao, LI Yikun, LI Xiaojun, YANG Shuwen. Noise-resistant change detection for remote sensing images based on spatial fuzzy C-means clustering and a Bayesian network. Remote Sensing for Natural Resources, 2023, 35(4): 96-104.

链接本文:

https://www.gtzyyg.com/CN/10.6046/zrzyyg.2022260 或 https://www.gtzyyg.com/CN/Y2023/V35/I4/96

Fig.1 SBN结构

Fig.2 算法流程图

Fig.3 实验图像及人工检测的变化结果

Fig.4 变化检测效果图(5%椒盐噪声)

Tab.1 变化检测模型性能比较(5%椒盐噪声)

Fig.5 变化检测效果图(零均值,方差0.01的高斯噪声)

Tab.2 变化检测模型性能比较(零均值,方差0.01高斯噪声)

Fig.6 变化检测效果图(0.5%椒盐噪声+零均值,方差0.001的高斯噪声)

Tab.3 变化检测模型性能比较(0.5%椒盐噪声+零均值,方差0.001的高斯噪声)

Fig.7 SVM-CVAPS、FCM-SBN-CVAPS算法和4种改进算法的噪声敏感度图

Tab.4 FLICM-SBN-CVAPS的噪声敏感度表(Kappa系数值)

Fig.8 模糊度敏感度图

Fig.9 训练样本数量敏感度图

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