Study of Fuzzy Based Classifier Parameter using Fuzzy Matrix
Rakesh Dwivedi1, Anil Kumar2, S. K. Ghosh3
1Rakesh Dwivedi, Deptt. Of Civil Engineering, Indian Institute of Technology Roorkee, Roorkee, India.
2Anil Kumar, Deptt. Of Photogramatry and Remote Sensing, Indian Institute of Remote Sensing Dehradun, Dehradun, India.
3S. K. Ghosh, Deptt. Of Civil Engineering, Indian Institute of Technology Roorkee, Roorkee, India.
Manuscript received on July 01, 2012. | Revised Manuscript received on July 04, 2012. | Manuscript published on July 05, 2012. | PP: 358-365 | Volume-2, Issue-3, July 2012. | Retrieval Number: C0818062312 /2012©BEIESP
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In the area of remote sensing, the decision making are not generally deterministic due to the involvement of fuzziness in the classification of remotely sensed imagery. A considerable number of identification errors are due to pixels that show an affinity with several information classes. The fuzzy concept is a valuable tool for dealing with classification problems. In remote sensing classification, fuzzy based classifiers are becoming increasingly popular. Due to the wide acceptance of fuzzy c-mean (FCM) and possibilistic c-means (PCM) classifiers, this has been used as a benchmark to evaluate the performance of other classifiers with optimized value of weighting exponent ‘m’ in this research. Evaluation of soft classification through FERM, SCM and Fuzzy kappa coefficient, using Euclidean norm based measures led to an improvement wherein FCM-Overall accuracy (MIN-LEAST) operator reflects higher classification accuracy, i.e., 97% and the value of Fuzzy Kappa coefficient is 0.97 with minimum uncertainty in it, for the optimized value of weighting exponent ‘m’ i.e. 4.0. In this experimentation two supervised classifiers namely FCM and PCM have been selected to demonstrate the improvement in the classification accuracy by FERM, SCM, MIN-MIN, MIN-LEAST, Fuzzy Kappa coefficient and uncertainty in SCM and Fuzzy Kappa coefficients.
Keywords: Fuzzy c-Mean (FCM), Fuzzy Error Matrix (FERM), Possiblistic c-Mean (PCM), Sub-pixel confusion-uncertainty matrix(SCM),