Abstract
A methodology to implement an automatic system for classifying remotely sensed data with an ongoing learning capability is introduced. The Nearest Neighbour (NN) rule is employed as the central classifier and several techniques are added to cope with the increase in computational load and with the risk of incorporating noisy data into the training sample. Experimental results confirm the enhancement in classification accuracy.
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