Abstract
|
Article Information:
Comparison of Soft Computing Approaches for Texture Based Land Cover Classification of Remotely Sensed Image
S. Jenicka and A. Suruliandi
Corresponding Author: S. Jenicka
Submitted: 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.
Key words: ELM, fuzzy k-NN, MTP, SVM , texture descriptor, ,
|
Abstract
|
PDF
|
HTML |
|
Cite this Reference:
S. Jenicka and A. Suruliandi, . Comparison of Soft Computing Approaches for Texture Based Land Cover Classification of Remotely Sensed Image. Research Journal of Applied Sciences, Engineering and Technology, (10): 1216-1226.
|
|
|
|
|
ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
|
Information |
|
|
|
Sales & Services |
|
|
|