面向异源影像的FCM-SBN-CVAPS多尺度变化检测方法

doi:10.13474/j.cnki.11-2246.2023.0357

测绘通报 ›› 2023, Vol. 0 ›› Issue (12): 45-50.doi: 10.13474/j.cnki.11-2246.2023.0357

• 学术研究 • 上一篇

面向异源影像的FCM-SBN-CVAPS多尺度变化检测方法

武锦沙¹, 杨树文^1,2,3, 李轶鲲^1,2,3, 赵志威¹, 郑耀¹, 付昱凯¹

1. 兰州交通大学测绘与地理信息学院, 甘肃兰州 730070;
2. 地理国情监测技术应用国家地方联合工程研究中心, 甘肃兰州 730070;
3. 甘肃省地理国情监测工程实验室, 甘肃兰州 730070

收稿日期:2023-03-06 发布日期:2024-01-08
通讯作者: 杨树文。E-mail:825198827@qq.com
作者简介:武锦沙(1999-),女,硕士生,主要研究方向为遥感数字图像智能处理。E-mail:1722746143@qq.com
基金资助:
国家重点研发计划(2022YFB3903604);中央引导地方科技发展资金(22ZY1QA005);国家自然科学基金(42161069)

FCM-SBN-CVAPS multi-scale target change detection based on multi-spectral heterologous remote sensing image

WU Jinsha¹, YANG Shuwen^1,2,3, LI Yikun^1,2,3, ZHAO Zhiwei¹, ZHENG Yao¹, FU Yukai¹

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

Received:2023-03-06 Published:2024-01-08

摘要/Abstract

摘要： 在遥感影像变化检测中,FCM-SBN-CVAPS方法可以有效处理混合像元问题,但由于异源影像之间存在较大差异,因此对于异源影像变化检测具有局限性。为了提高变化检测的精度,本文在模糊C均值聚类(FCM)、简单贝叶斯网络(SBN)和后验概率空间变化向量分析(CVAPS)方法基础上,提出了一种面向异源影像的FCM-SBN-CVAPS多尺度变化检测方法。首先,通过图像增强改善图像质量;其次,为提高FCM-SBN后验概率向量计算的准确度,对同物异谱、异物同谱的区域进行有效判断,将子类地物样本进行组合构成复合类型地物样本,实现FCM-SBN-CVAPS大目标变化检测;然后,使用子类样本对漏检的区域进行重新检测,实现小目标变化检测,并叠加不同尺度变化信息,以获得最终的变化检测结果;最后,利用两组异源影像数据对提出的方法进行对比验证。结果表明,该方法可以降低错检率、漏检率,总体精度和Kappa系数均高于对比方法。

关键词: 变化检测, 图像增强, 模糊C均值聚类, 简单贝叶斯网络, 异源影像

Abstract: In remote sensing image change detection, FCM-SBN-CVAPS method can effectively deal with the problem of mixed pixels. However, due to the large differences between heterogeneous images, it has limitations for heterogeneous image change detection. In order to improve the accuracy of change detection, in fuzzy C-means (FCM), simple Bayesian network, (SBN) and change vector analysis in posterior probability space (CVAPS), In this paper, a multi-scale change detection method of FCM-SBN-CVAPS for heterologous images is proposed. Firstly, image quality is improved by image enhancement. Secondly, in order to improve the accuracy of the posterior probability vector calculation of FCM-SBN, and effectively judge the areas with different spectra of the same object and the same spectrum of foreign objects, the subclasses of ground object samples are combined to form compound type ground object samples, and the large target change detection of FCM-SBN-CVAPS is realized. At the same time, subclass samples are used to redetect the missed areas to realize small target change detection, and the change information of different scales is superimposed to obtain the final change detection result. Finally,two groups of heterosource image data are used to compare and verify the proposed method. The results show that the proposed method can reduce the false detection rate and missed detection rate, and the overall accuracy and Kappa coefficient are higher than the comparison method.

Key words: change detection, image enhancement, fuzzy C-means, simple Bayesian network, heterogeneous image

中图分类号:

P237

武锦沙, 杨树文, 李轶鲲, 赵志威, 郑耀, 付昱凯. 面向异源影像的FCM-SBN-CVAPS多尺度变化检测方法[J]. 测绘通报, 2023, 0(12): 45-50.

WU Jinsha, YANG Shuwen, LI Yikun, ZHAO Zhiwei, ZHENG Yao, FU Yukai. FCM-SBN-CVAPS multi-scale target change detection based on multi-spectral heterologous remote sensing image[J]. Bulletin of Surveying and Mapping, 2023, 0(12): 45-50.

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面向异源影像的FCM-SBN-CVAPS多尺度变化检测方法

FCM-SBN-CVAPS multi-scale target change detection based on multi-spectral heterologous remote sensing image

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