Remote Sensing-based Change Detection using Change Vector Analysis in Posterior Probability Space: A Context-Sensitive Bayesian Network Approach

• Published in: IEEE journal of selected topics in applied earth observations and remote sensing Vol. 16; pp. 1 - 21
• Main Authors: Li, Yikun, Li, Xiaojun, Song, Jiaxin, Wang, Zihao, He, Yi, Yang, Shuwen
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.01.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Analysis; Bayes methods; Bayesian analysis; Bayesian theory; Change detection; Change vector analysis; Change vector analysis (CVA); Change vector analysis in posterior probability space; change vector analysis in posterior probability space (CVAPS); Classification; Clustering; Clustering algorithms; Conditional probability; Context; Context-sensitive Bayesian network; context-sensitive bayesian network (CSBN); Detection; Effectiveness; Fuzzy C means; fuzzy C means (FCM); Ground cover; Image enhancement; Image resolution; Pixels; Post-classification change detection; post-classification change detection (PCC); Probability theory; Radiometric correction; Radiometry; Remote sensing; Spatial data; Spatial resolution; Support vector machines; Uncertainty; Vector analysis
• DOI: 10.1109/JSTARS.2023.3260112
• ISSN: 1939-1404

Change vector analysis (CVA) and post-classification change detection (PCC) have been the most widely used change-detection methods. However, CVA requires sound radiometric correction to achieve optimal performance, and PCC is susceptible to accumulated classification errors. Although change vector analysis in the posterior probability space (CVAPS) was developed to resolve the limitations of PCC and CVA, the uncertainty of remote sensing imagery limits the performance of CVAPS owing to three major problems: 1) mixed pixels, 2) identical ground cover type with different spectra, and 3) different ground cover types with the same spectrum. To address this problem, this study proposes the FCM-CSBN-CVAPS approach under the CVAPS framework. The proposed approach decomposes the mixed pixels into multiple signal classes using the fuzzy C means (FCM) algorithm. Although the mixed pixel problem is less severe in the high-resolution image, the change detection performance is still enhanced because, as a soft clustering algorithm, FCM is less susceptible to cumulative clustering error. Then, a context-sensitive Bayesian network (CSBN) is constructed to establish multiple-to-multiple stochastic linkages between signal pairs and ground cover types by incorporating spatial information to resolve problems 2 and 3 discussed above. Finally, change detection is performed using CVAPS in the posterior probability space. The effectiveness of the proposed approach is evaluated on three bi-temporal remote sensing datasets with different spatial sizes and resolutions. The experimental results confirm the effectiveness of FCM-CSBN-CVAPS in addressing the uncertainty problems of change detection and its superiority over other relevant change-detection techniques.