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Unsupervised classification for multi-sensor data in remote sensing using markov random field and maximum entropy method

Sanghoon Lee(1) and Melba M. Crawford (2)

(1): Dept. of Industrial Engineering, Kyung Won University, Seongnam, South Korea
(2): Center for Space Research, University of Texas at Austin



This study employs a multi-stage algorithm that makes use of spatial contextual information in a hierarchical clustering procedure for unsupervised image segmentation. Hierarchical clustering is based on similarity measures between all pairs of candidates being considered for merging. The multi-stage algorithm involves local segmentor and global segmentor. The data from individual sensors are integrated into a set of multidimensional data and it is then applied to the hierarchical clustering algorithm based on linear statistics under the assumption of an additive noise model.


Last Modified: Tue July 13, 1999