Fast hybrid rough-set theoretic fuzzy clustering technique with application to multispectral image segmentation

  • Authors:

  • Arpit Srivastava;Abhinav Asati;Mahua Bhattacharya

  • Affiliations:

  • MA National Institute of Technology, Bhopal, India;MA National Institute of Technology, Bhopal, India;ABV Indian Institute of Information Technology and Management, Gwalior, Madhya Pradesh, India

  • Venue:
  • Proceedings of the First International Conference on Intelligent Interactive Technologies and Multimedia
  • Year:
  • 2010

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Abstract

Remotely sensed Multispectral Images are of high significance, for the analysis of landscape change detection and land use/cover classification etc. A Novel method for Segmentation, of such images is presented in this paper. The proposed method collaborates the Fuzzy Clustering Algorithm with Rough Set theoretic approach and a convergence improving Mechanism. Hybridization of Fuzzy Sets with Rough Sets results into an unsupervised framework which can handle uncertainties associated with the process, while allowing overlapping of partitions at the same time. But this process is time taking, due to highly correlated nature of dataset, thereby increasing the computation time. Therefore, the Fuzzy- Rough hybrid clustering is supplemented with a suppression mechanism to enhance the speed of convergence, which eventually reduces the time of computation. The conducted experiments demonstrate the merits of the proposed algorithm in segmenting small objects and sharp boundaries, which plays an important role in remote-sensing image segmentation applications. The comparison of proposed method with other similar models is presented in terms of time of computation, to prove the suitability of presented method for real-time applications.