Abstract
This paper presents an unique Possibilistic c-Means with constraints (PCM-S) algorithms in a supervised way. This algorithm overcome the disadvantage of Possibilistic c-Means (PCM) algorithm by incorporating local information through spatial constraints to control the effect of neighboring terms. PCM-S has been deployed by adding spatial constraints in order to provide robustness to noise and outliers. FORMOSAT-2 satellite imagery of Haridwar city has been used and classified result is tested with Mean Membership Difference method.
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