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A context-sensitive Bayesian technique for the partially supervised classification of multitemporal images

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3 Author(s)
Cossu, R. ; Dept. of Inf. & Commun. Technol., Univ. of Trento, Italy ; Chaudhuri, S. ; Bruzzone, L.

An advanced context-sensitive classification technique that exploits a temporal series of remote sensing images for a regular updating of land-cover maps is proposed. This technique extends the use of spatio-contextual information to the framework of partially supervised approaches (that are capable of addressing the updating problem under the realistic, though critical, constraint that no ground-truth information is available for some of the images to be classified). The proposed classifier is based on an iterative partially supervised algorithm that jointly estimates the class-conditional densities and the prior model for the class labels on the image to be classified by taking into account spatio-contextual information. Experimental results point out that the proposed technique is effective and that it significantly outperforms the context-insensitive partially supervised approaches presented in the literature.

Published in:

Geoscience and Remote Sensing Letters, IEEE  (Volume:2 ,  Issue: 3 )