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

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 sca...