Computer Science – Computer Vision and Pattern Recognition
Scientific paper
2009-11-23
IEEE Transactions on Geoscience and Remote Sensing, Vol. 44, No. 6, pp. 1633-1642, June 2006
Computer Science
Computer Vision and Pattern Recognition
14 pages, 2 figures
Scientific paper
Land cover classification using multispectral satellite image is a very challenging task with numerous practical applications. We propose a multi-stage classifier that involves fuzzy rule extraction from the training data and then generation of a possibilistic label vector for each pixel using the fuzzy rule base. To exploit the spatial correlation of land cover types we propose four different information aggregation methods which use the possibilistic class label of a pixel and those of its eight spatial neighbors for making the final classification decision. Three of the aggregation methods use Dempster-Shafer theory of evidence while the remaining one is modeled after the fuzzy k-NN rule. The proposed methods are tested with two benchmark seven channel satellite images and the results are found to be quite satisfactory. They are also compared with a Markov random field (MRF) model-based contextual classification method and found to perform consistently better.
Das Jayajit
Laha Arijit
Pal Nikhil R.
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