Fuzzy-based approach to incorporate spatial constraints in possibilistic c-means algorithm for remotely sensed imagery Online publication date: Wed, 09-Sep-2020
by Abhishek Singh; Anil Kumar
International Journal of Intelligent Information and Database Systems (IJIIDS), Vol. 13, No. 2/3/4, 2020
Abstract: This paper presents a robust possibilistic c-means with constraints (PCM-S) algorithms in a supervised way for remotely sensed imagery. The PCM-S overcome the disadvantages of PCM, by incorporating local information through spatial constraints to control the effect of neighbouring terms. PCM-S has been deployed by adding spatial constraints in order to provide robustness to noise and outliers. Neighbourhood labelling has been done in PCM-S by introducing local window (NR) and regulariser parameter (ηi). Experiments have been conducted on Formosat-2 satellite imagery of Haridwar area in which classified results of PCM and PCM-S is optimised using mean membership difference (MMD) method and performance of classifiers are analysed using root mean square (RMSE) method. Experiments performed on 1% salt and pepper noisy image and original image show that PCM-S classifier is effective in minimising noisy pixels which produces least RMSE than PCM.
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