Multivariate alteration detection using angle thresholds based on a context-sensitive Bayesian network

doi:10.6046/zrzyyg.2024267

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Abstract

In the field of multivariate alteration detection （MAD） of remote sensing images，change vector analysis in posterior probability space （CVAPS） is a widely used method. However，the CVAPS，which employs support vector machines to estimate the posterior probability vectors of remote sensing image pixels，is susceptible to various factors such as different objects with the same spectrum，the same object with different spectra，and mixed pixels in remote sensing images. These factors make it difficult to accurately estimate the magnitude and direction of the posterior probability vectors of complex pixels，consequently affecting the accuracy of multivariate alteration detection. Therefore，under the framework of CVAPS，this paper proposed a MAD method using angle thresholds，which employed the fuzzy C-means clustering to decompose mixed pixels and coupled a context-sensitive Bayesian network. When the angle is less than a certain threshold，the pixel is identified as the change type represented by the standard change vector. Experimental results show that the proposed algorithm exhibited superior alteration detection performance，achieving higher change detection accuracy than other algorithms.

Keywords angle threshold multivariate alteration detection （MAD） fuzzy C-means （FCM） context-sensitive Bayesian network posterior probability space change vector analysis （CVA）

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TP751

Issue Date: 28 October 2025

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	Rui ZHU
	Yikun LI
	Xiaojun LI
	Shuwen YANG
	Jiangling XIE

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Rui ZHU,Yikun LI,Xiaojun LI, et al. Multivariate alteration detection using angle thresholds based on a context-sensitive Bayesian network[J]. Remote Sensing for Natural Resources, 2025, 37(5): 131-140.

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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2024267 OR https://www.gtzyyg.com/EN/Y2025/V37/I5/131

Fig.1 Flowchart of FCM-CSBN-CVAPS-AT algorithm

Tab.1 Number of feature types in each study area （个）

Fig.2 Comparison of change detection algorithms of study area 1

Fig.3 Comparison of change detection algorithms of study area 2

Fig.4 Comparison of change detection algorithms of study area 3

Fig.5 The influence of angle threshold on Kappa coefficient of changing features in study area 2

Tab.2 Performance results of change detection algorithms in study area 1 and study area 2

Tab.3 Performance results of change detection algorithms in study area 3

[1]	李德仁. 利用遥感影像进行变化检测[J]. 武汉大学学报(信息科学版), 2003, 28(s1):7-12.
[1]	Li D R. Change detection from remote sensing images[J]. Geoma-tics and Information Science of Wuhan University, 2003, 28(s1):7-12.
[2]	李天宏, 韩鹏. 厦门市土地利用/覆盖动态变化的遥感检测与分析[J]. 地理科学, 2001, 21(6):537-543.
[2]	Li T H, Han P. Land use/cover change detection and analysis with remote sensing in Xiamen City[J]. Scientia Geographica Sinica, 2001, 21(6):537-543. doi: 10.13249/j.cnki.sgs.2001.06.537
[3]	严宇, 刘耀林. 基于融合和IFLICM算法的非监督遥感影像变化检测[J]. 测绘通报, 2018(3):25-31. doi: 10.13474/j.cnki.11-2246.2018.0070
[3]	Yan Y, Liu Y L. Unsupervised remote sensing image change detection based on fusion and IFLICM algorithm[J]. Bulletin of Surveying and Mapping, 2018(3):25-31. doi: 10.13474/j.cnki.11-2246.2018.0070
[4]	宋嘉鑫, 李轶鲲, 杨树文, 等. 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.doi:10.6046/zrzyyg.2023079.
[4]	Song J X, Li Y K, Yang S W, et al. NSCT-based change detection for high-resolution remote sensing images under the framework of change vector analysis in posterior probability space[J]. Remote Sensing for Natural Resources, 2024, 36(3):128-136.doi:10.6046/zrzyyg.2023079.
[5]	谢江陵, 李轶鲲, 李小军, 等. 基于耦合空间模糊C均值聚类和推土机距离的变化检测[J]. 遥感信息, 2024, 39(3):144-152.
[5]	Xie J L, Li Y K, Li X J, et al. Change detection using coupling spatial fuzzy C-means clustering and earth mover’s distance[J]. Remote Sensing Information, 2024, 39(3):144-152.
[6]	Song J X, Li Y K, Li X J, et al. Unsupervised remote sensing image classification with differentiable feature clustering by coupled transformer[J]. Journal of Applied Remote Sensing, 2024, 18(2):026505.
[7]	赵鹤婷, 李小军, 徐欣钰, 等. 基于ICM的高光谱图像自适应全色锐化算法[J]. 自然资源遥感, 2024, 36(2):97-104.doi:10.6046/zrzyyg.2023026.
[7]	Zhao H T, Li X J, Xu X Y, et al. An ICM-based adaptive pansharpening algorithm for hyperspectral images[J]. Remote Sensing for Natural Resources, 2024, 36(2):97-104.doi:10.6046/zrzyyg.2023026.
[8]	Gao X, Xiong H K. Unsupervised representation learning with prior-free and adversarial mechanism embedded autoencoders[C]// 2018 IEEE International Conference on Multimedia and Expo (ICME). IEEE,2018:1-6.
[9]	Liang C F, Chen Z. A self-supervised hierarchical clustering network for multiple change detection in multitemporal hyperspectral images[C]// 2022 12th Workshop on Hyperspectral Imaging and Signal Processing:Evolution in Remote Sensing (WHISPERS). IEEE,2022:1-4.
[10]	盛光伟. 基于角度优先变化向量分析的林地变化检测[D]. 南京: 南京大学, 2020.
[10]	Sheng G W. Forest change detection based on direction-first change vector analysis[D]. Nanjing: Nanjing University, 2020.
[11]	Li L, Li X, Zhang Y, et al. Change detection for high-resolution remote sensing imagery using object-oriented change vector analysis method[C]// 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). IEEE,2016:2873-2876.
[12]	Chen J, Chen X H, Cui X H, et al. Change vector analysis in posterior probability space:A new method for land cover change detection[J]. IEEE Geoscience and Remote Sensing Letters, 2011, 8(2):317-321.
[13]	Melgani F, Bruzzone L. Classification of hyperspectral remote sens-ing images with support vector machines[J]. IEEE Transactions on Geoscience and Remote Sensing, 2004, 42(8):1778-1790.
[14]	Lantzanakis G, Mitraka Z, Chrysoulakis N. X-SVM:An extension of C-SVM algorithm for classification of high-resolution satellite imagery[J]. IEEE Transactions on Geoscience and Remote Sens-ing, 2021, 59(5):3805-3815.
[15]	龙亦凡, 乔雯钰, 孙静. 基于SVM的大屯矿区遥感影像变化检测[J]. 测绘与空间地理信息, 2020, 43(12):107-110,115.
[15]	Long Y F, Qiao W Y, Sun J. Change detection of remote sensing images in Datun mining area based on support vector machine[J]. Geomatics & Spatial Information Technology, 2020, 43(12):107-110,115.
[16]	Li Y K, Li X J, Song J X, et al. Remote-sensing-based change detection using change vector analysis in posterior probability space:A context-sensitive Bayesian network approach[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023, 16:3198-3217.
[17]	李轶鲲, 杨洋, 杨树文, 等. 耦合模糊C均值聚类和贝叶斯网络的遥感影像后验概率空间变化向量分析[J]. 自然资源遥感, 2021, 33(4):82-88.doi:10.6046/zrzyyg.2021032.
[17]	Li Y K, Yang Y, Yang S W, et al. A change vector analysis in posterior probability space combined with fuzzy C-means clustering and a Bayesian network[J]. Remote Sensing for Natural Resources, 2021, 33(4):82-88.doi:10.6046/zrzyyg.2021032.
[18]	Li Y K, Yang S W, Liu T, et al. Comparative assessment of semantic-sensitive satellite image retrieval:Simple and context-sensitive Bayesian networks[J]. International Journal of Geographical Information Science, 2012, 26(2):247-263.
[19]	Otsu N. A threshold selection method from gray-level histograms[J]. IEEE Transactions on Systems,Man,and Cybernetics, 1979, 9(1):62-66.
[20]	Saha S, Bovolo F, Bruzzone L. Unsupervised deep change vector analysis for multiple-change detection in VHR images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(6):3677-3693.

[1]	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[J]. Remote Sensing for Natural Resources, 2024, 36(3): 128-136.
[2]	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[J]. Remote Sensing for Natural Resources, 2023, 35(4): 96-104.
[3]	LI Yikun, YANG Yang, YANG Shuwen, WANG Zihao. A change vector analysis in posterior probability space combined with fuzzy C-means clustering and a Bayesian network[J]. Remote Sensing for Natural Resources, 2021, 33(4): 82-88.
[4]	Xinran ZHU, Bo WU, Qiang ZHANG. An improved CVAPS method for automatic updating of LUCC classification[J]. Remote Sensing for Land & Resources, 2018, 30(2): 29-37.
[5]	HU Yuxi, LI Yikun, YANG Ping. Retrieving of remote sensing images based on content-sensitive Bayesian networks and direction relations[J]. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(3): 70-76.

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