Download This PaperFuzzy Based Approach to Incorporate Spatial Constraints in Possibilistic c-Means Algorithm for Remotely Sensed Imagery
6 Pages Posted: 12 Jun 2019
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: 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
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