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Combining unsupervised and knowledge-based methods in large-scale forest classification

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4 Author(s)
Shuan Quegan ; Centre for Earth Obs. Sci., Sheffield Univ., UK ; Jiong Jiong Yu ; Balzter, H. ; LeToan, T.

Data analysis and physical reasoning, frame-to-frame variability, and the need to minimise operator interaction because of the large number of frames, led the authors to develop a fully automatic classification scheme based on ISODATA concepts within the SIBERIA project. This used a multi-variate Gaussian model for the data, and was adapted to accept different initialisation procedures and to be able to form both maximum likelihood and maximum a posteriori classifications. A further improvement was to use its output to drive an iterated contextual classifier, hence exploiting spatial information

Published in:

Geoscience and Remote Sensing Symposium, 2000. Proceedings. IGARSS 2000. IEEE 2000 International  (Volume:1 )

Date of Conference:

2000

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