title: A temporally adaptive classifier for multispectral imagery.
abstract: This paper presents a new temporally adaptive classification system for multispectral images. A spatial-temporal adaptation mechanism is devised to account for the changes in the feature space as a result of environmental variations. Classification based upon spatial features is performed using Bayesian framework or probabilistic neural networks (PNNs) while the temporal updating takes place using a spatial-temporal predictor. A simple iterative updating mechanism is also introduced for adjusting the parameters of these systems. The proposed methodology is used to develop a pixel-based cloud classification system. Experimental results on cloud classification from satellite imagery are provided to show the usefulness of this system.
authors.affiliation: Department of Electrical and Computer Engineering. Colorado State University, Fort Collins, CO 80523, USA.
authors.forename: Jianqi
authors.initials: J
authors.lastname: Wang
authors.forename: Mahmood R
authors.initials: MR
authors.lastname: Azimi-Sadjadi
authors.forename: Donald
authors.initials: D
authors.lastname: Reinke
mesh_terms: D007091:Image Processing, Computer-Assisted / Q000379:methods* / Q000592:standards; D013997:Time Factors