Fuzzy Based Approach to Incorporate Spatial Constraints in Possibilistic c-Means Algorithm for Remotely Sensed Imagery

6 Pages Posted: 12 Jun 2019

See all articles by Abhishek Singh

Abhishek Singh

Indian Institute of Remote Sensing (IIRS), Indian Space Research Organisation (ISRO)

Anil Kumar

Indian Institute of Remote Sensing (IIRS), Indian Space Research Organisation (ISRO)

Date Written: February 20, 2019

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.

Suggested Citation

Singh, Abhishek and Kumar, Anil, Fuzzy Based Approach to Incorporate Spatial Constraints in Possibilistic c-Means Algorithm for Remotely Sensed Imagery (February 20, 2019). Proceedings of International Conference on Sustainable Computing in Science, Technology and Management (SUSCOM), Amity University Rajasthan, Jaipur - India, February 26-28, 2019, Available at SSRN: https://ssrn.com/abstract=3354465 or http://dx.doi.org/10.2139/ssrn.3354465

Abhishek Singh (Contact Author)

Indian Institute of Remote Sensing (IIRS), Indian Space Research Organisation (ISRO)

Dehradun
India

Anil Kumar

Indian Institute of Remote Sensing (IIRS), Indian Space Research Organisation (ISRO) ( email )

Dehradun
India

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