基于后验概率空间变化向量分析的NSCT高分辨率遥感影像变化检测

doi:10.6046/zrzyyg.2023079

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摘要

非下采样轮廓波变换(non-subsampled contourlet transform,NSCT)和变化向量分析法(change vector analysis,CVA)在高分辨率遥感影像变化检测中,会因不同地物的变化幅度有显著差异,而在单一阈值下无法保证较高的检测精度。为此,文章在后验概率空间变化向量分析(change vector analysis in posterior probability space,CVAPS)的框架下,提出了一种基于模糊C均值聚类(fuzzy C-means, FCM)和简单贝叶斯网络(simple Bayesian network,SBN)的NSCT变化检测方法(FCM-SBN-CVAPS-NSCT)。该方法首先将FCM与SBN耦合,计算出后验概率变化强度图; 之后,通过NSCT将后验概率变化强度图分解为不同尺度和方向的子图,通过保留高频子图中的细节并消除噪声,优化了重构后的后验概率变化强度图,实现了后验概率空间下的多尺度、多方向的变化检测,最终提高了变化检测的精度。实验结果表明,所提方法在3个研究区中得到的Kappa系数比FCM-SBN-CVAPS分别高出了0.100 9,0.056 6和0.067 4,具有一定的优越性。

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	宋嘉鑫
	李轶鲲
	杨树文
	李小军

关键词 ：简单贝叶斯网络, 模糊C均值聚类, 后验概率空间变化向量分析, 非下采样轮廓波变换

Abstract：

In the change detection for high-resolution remote sensing images, non-subsampled contourlet transform (NSCT) and change vector analysis (CVA) cannot ensure high detection accuracies under single thresholds due to significantly different changes in surface features. Hence, under the framework of change vector analysis in posterior probability space (CVAPS), this study proposed a NSCT-based change detection method combining fuzzy C-means (FCM) clustering and a simple Bayesian network (SBN): the FCM-SBN-CVAPS-NSCT method. First, the proposed method coupled FCM with an SBN to generate a change intensity map in posterior probability space. Then, the change intensity map was decomposed into submaps of different scales and directions through NSCT. The reconstructed change intensity map was optimized by preserving the details and eliminating noise in the high-frequency submaps. Finally, the multi-scale and multi-directional change detection in posterior probability space was achieved, enhancing the change detection accuracy. As indicated by the experimental results, the Kappa values obtained by the proposed method for three study areas were 0.100 9, 0.056 6, and 0.067 4 higher than those derived from the FCM-SBN-CVAPS method, demonstrating certain superiority.

Key words： simple Bayesian network fuzzy C-means clustering change vector analysis in posterior probability space non-subsampled contourlet transform

收稿日期: 2023-03-21 出版日期: 2024-09-03

ZTFLH:

TP79

基金资助:国家重点研发计划项目“边海重点区域安全态势异常感知与互联互通分析技术”(2022YFB3903604);国家自然科学基金项目“西北重点城市彩钢板建筑群与产业园区时空关联关系”(42161069)

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

作者简介: 宋嘉鑫(1997-),男,硕士研究生,研究方向为遥感图像处理。Email: 3012571995@qq.com。

引用本文:

宋嘉鑫, 李轶鲲, 杨树文, 李小军. 基于后验概率空间变化向量分析的NSCT高分辨率遥感影像变化检测[J]. 自然资源遥感, 2024, 36(3): 128-136.
SONG Jiaxin, LI Yikun, YANG Shuwen, LI Xiaojun. NSCT-based change detection for high-resolution remote sensing images under the framework of change vector analysis in posterior probability space. Remote Sensing for Natural Resources, 2024, 36(3): 128-136.

链接本文:

https://www.gtzyyg.com/CN/10.6046/zrzyyg.2023079 或 https://www.gtzyyg.com/CN/Y2024/V36/I3/128

Fig.1 FCM-SBN-CVAPS-NSCT算法流程

Fig.2 简单贝叶斯网络

Fig.3 NSCT三阶段分解结构

Fig.4 研究区1变化检测算法比较

Fig.5 研究区2变化检测算法比较

Fig.6 研究区3变化检测算法比较

Fig.7 不同数量训练像素数对算法Kappa系数的影响

Fig.8 不同聚类数和模糊参数q对Kappa系数的影响

Fig.9 NSCT去噪阈值参数对Kappa系数的影响

Tab.1 NSCT分解层数对Kappa系数的影响

Tab.2 研究区1变化检测算法性能比较

Tab.3 研究区2变化检测算法性能比较

Tab.4 研究区3变化检测算法性能比较

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