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

doi:10.13474/j.cnki.11-2246.2023.0357

Abstract

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

CLC Number:

P237

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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