Introduction of Local Spatial Constraints and Local Similarity Estimation in Possibilistic c-Means Algorithm for Remotely Sensed Imagery

Abhishek Singh, Anil Kumar
2019 Journal of Modeling and Optimization  
This paper presents a unique Possibilistic c-Means with constraints (PCM-S) with Adaptive Possibilistic Local Information c-Means (ADPLICM) in a supervised way by incorporating local information through local spatial constraints and local similarity measures in Possibilistic c-Means Algorithm. PCM-S with ADPLICM overcome the limitations of the known Possibilistic c-Means (PCM) and Possibilistic c-Means with constraints (PCM-S) algorithms. The major contribution of proposed algorithm to ensure
more » ... e noise resistance in the presence of random salt & pepper noise. The effectiveness of proposed algorithm has been analysed on random "salt and pepper" noise added on original dataset and Root Mean Square Error (RMSE) has been calculated between original dataset and noisy dataset. It has been observed that PCM-S with ADPLICM is effective in minimizing noise during supervised classification by introducing local convolution.
doi:10.32732/jmo.2019.11.1.51 fatcat:voxfdzm5a5ecpenzi42epazlri