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

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。

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.