Research Article | OPEN ACCESS
Comparison of Soft Computing Approaches for Texture Based Land Cover Classification of Remotely Sensed Image
1S. Jenicka and 2A. Suruliandi
1Einstein College of Engineering
1Department of Computer Science and Engineering, Manonmaniam Sundaranar University,
Tirunelveli-627 012, Tamil Nadu, India
Research Journal of Applied Sciences, Engineering and Technology 2015 10:1216-1226
Received: March 23, 2015 | Accepted: April 22, 2015 | Published: August 05, 2015
Abstract
Texture feature is a predominant feature in land cover classification of remotely sensed images. In this study, texture features were extracted using the proposed multivariate descriptor, Multivariate Ternary Pattern (MTP). The soft classifiers such as Fuzzy k-Nearest Neighbor (Fuzzy k-NN), Support Vector Machine (SVM) and Extreme Learning Machine (ELM) were used along with the proposed multivariate descriptor for performing land cover classification. The experiments were conducted on IRS P6 LISS-IV data and the results were evaluated based on error matrix, classification accuracy and Kappa statistics. From the experiments, it was found that the proposed descriptor with SVM classifier gave 93.04% classification accuracy.
Keywords:
ELM , fuzzy k-NN , MTP , SVM , texture descriptor,
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Competing interests
The authors have no competing interests.
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