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

Amarendra Pratap Singh ,Anil Kumar

doi:10.32732/jmo.2019.11.1.51

Amarendra Pratap Singh , Anil Kumar

Open Access

https://doi.org/10.32732/jmo.2019.11.1.51

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Abstract

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 the 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.

Highlights

Introduction of Local Spatial Constraints andLocal Similarity Estimation in Possibilistic c-Means Algorithm for Remotely Sensed ImageryAbhishek Singh*, Anil KumarIndian Institute of Remote Sensing (IIRS), Indian Space Research Organisation (ISRO), Dehradun, 248001, IndiaReceived: 5 March 2019; Accepted: 25 March 2019; Available online: 10 June 2019 AbstractThis 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
Zhang et al [17] have used Local Similarity Measure based on Pixel Spatial Attraction Model in Adaptive Fuzzy Local Information c-means which adaptively determines the weighting factors for neighboring, the same way we have been applied for PCM-S with ADPLICM based fuzzy classifier
A novel PCM-S with ADPLICM algorithm was introduced for image classification and overcome the disadvantages of PCM and PCM-S algorithms

Summary

Introduction

Remote Sensing technology has been widely used to obtain useful information for extraction and discrimination of land cover by assigning a class label to each pixel in a digital image. Chen et al [15] proposed FCM_S1 and FCM_S2, two variants of FCM_S algorithm in order to resolve the problem of FCM_S by introducing mean and median filtered image respectively to replace the neighborhood term of FCM_S. In all these FCM based algorithms needs a crucial parameter to control the trade-off between the robustness to noise and the effectiveness of preserving the image details.

Mathematical concept of preliminary algorithm

Mathematical concept of PCM-S with ADPLICM algorithm

Experimental results

Conclusion