Fuzzy-based approach to incorporate spatial constraints in possibilistic c-means algorithm for remotely sensed imagery. (25th August 2020) - Singh, Abhishek; Kumar, Anil
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.