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

Employs a multi-stage algorithm that makes use of spatial contextual information in a hierarchical clustering procedure for unsupervised image segmentation. The hierarchical clustering algorithm is based on similarity measures between all pairs of candidates being considered for merging. The multi-s...

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Bibliographic Details
Published in1999 IEEE International Geoscience and Remote Sensing Symposium Vol. 2; pp. 1200 - 1202 vol.2
Main Authors Sanghoon Lee, Crawford, M.M.
Format Conference Proceeding
LanguageEnglish
Published IEEE 1999
Subjects
Bayesian methods
Clustering algorithms
Digital images
Entropy
Geophysical measurements
Image segmentation
Image sensors
Layout
Markov random fields
Remote sensing
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ISBN0780352076
9780780352070
DOI10.1109/IGARSS.1999.774577

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Summary:Employs a multi-stage algorithm that makes use of spatial contextual information in a hierarchical clustering procedure for unsupervised image segmentation. The hierarchical clustering algorithm is based on similarity measures between all pairs of candidates being considered for merging. The multi-stage algorithm involves a local segmentor and a 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.
ISBN:0780352076
9780780352070
DOI:10.1109/IGARSS.1999.774577