SVM Parameter Optimization using ALO for Object Based Land Cover Classification
K.Jayanthi1, L.R. Sudha2
1K.Jayanthi*, Assistant Professor, Department of Computer Application, Govt. Arts College, Chidambaram, India,
2L.R.Sudha, Associate Professor, Department of Computer Science & Engineering, Annamalai University, Annamalainagar.
Manuscript received on January 01, 2020. | Revised Manuscript received on January 20, 2020. | Manuscript published on January 30, 2020. | PP: 2968-2972 | Volume-8 Issue-5, January 2020. | Retrieval Number: E6579018520/2020©BEIESP | DOI: 10.35940/ijrte.E6579.018520
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© The Authors. 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: Machine Learning algorithms are often used to solve various kinds of data classification task. Support Vector Machine (SVM) performs better for object oriented classification of high dimensional remote sensing datasets even with minimum training samples. In order to obtain improved performance in classification, the generalization and learning ability of SVM can be enriched by proper tuning of kernel and penalizing parameters of SVM. In this methodology ALO optimizer performs the optimal searching of SVM parameter in the direction of reducing misclassification rate. The proposed approach results better SVM parameters for the significant feature sub set which characterize the Landsat image objects of the study area. Performance of ALO is compared with GA based SVM parameter optimization. Accurate thematic classification map of land cover classes of the area of study also resulted in this module.
Keywords: SVM, Parameter tuning, Land cover classification, ALO algorithm, Object based classification.
Scope of the Article: Parallel and Distributed Algorithms.