A supervised Bayesian approach for simultaneous segmentation and classification

This paper presents a new paradigm for object based classification of multispectral images. Instead of classifying objects only after the segmentation process is completed, it is proposed to intercept the early stages of the segmentation by iteratively performing classification tests to under growing regions. By applying this simultaneous analysis, mislabeling of objects considered only after segmentation is completely done can be avoided. The proposed technique assumes that some growing regions can present higher membership to a particular class when comparing to the final object in which it is included. A Bayesian framework was applied in classification tests performed by pixel based, traditional object based, and the proposed technique were performed. The results show the soundness of the proposed method when comparing overall accuracies with a reference map.