An Unsupervised Remote Sensing Image Change Detection Method Based on RVMamba and Posterior Probability Space Change Vector

• Published in: Remote sensing (Basel, Switzerland) Vol. 16; no. 24; p. 4656
• Main Authors: Song, Jiaxin, Yang, Shuwen, Li, Yikun, Li, Xiaojun
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.12.2024
• Subjects: Accuracy; Algorithms; Bayesian analysis; Change detection; Classification; Clustering; Conditional probability; context-sensitive Bayesian network (CSBN); Datasets; Deep learning; Errors; Image processing; Image quality; Image resolution; Image segmentation; Mamba; Methods; Modules; Neural networks; Pixels; Radiometric correction; Remote sensing; Semantics; Spatial data; Support vector machines; Target detection; Training; unsupervised change vector analysis in posterior probability space; Unsupervised learning; Vector analysis; China
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs16244656

Change vector analysis in posterior probability space (CVAPS) is an effective change detection (CD) framework that does not require sound radiometric correction and is robust against accumulated classification errors. Based on training samples within target images, CVAPS can generate a uniformly scaled change-magnitude map that is suitable for a global threshold. However, vigorous user intervention is required to achieve optimal performance. Therefore, to eliminate user intervention and retain the merit of CVAPS, an unsupervised CVAPS (UCVAPS) CD method, RFCC, which does not require rigorous user training, is proposed in this study. In the RFCC, we propose an unsupervised remote sensing image segmentation algorithm based on the Mamba model, i.e., RVMamba differentiable feature clustering, which introduces two loss functions as constraints to ensure that RVMamba achieves accurate segmentation results and to supply the CSBN module with high-quality training samples. In the CD module, the fuzzy C-means clustering (FCM) algorithm decomposes mixed pixels into multiple signal classes, thereby alleviating cumulative clustering errors. Then, a context-sensitive Bayesian network (CSBN) model is introduced to incorporate spatial information at the pixel level to estimate the corresponding posterior probability vector. Thus, it is suitable for high-resolution remote sensing (HRRS) imagery. Finally, the UCVAPS framework can generate a uniformly scaled change-magnitude map that is suitable for the global threshold and can produce accurate CD results. The experimental results on seven change detection datasets confirmed that the proposed method outperforms five state-of-the-art competitive CD methods.