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Enhanced Classification Algorithms for the Satellite Image Processing


Affiliations
1 School of Computing Science and Engineering, VIT University, Vellore - 632014, India
2 School of Electronics and Communication Engineering, VIT University, Vellore - 632014, India
 

Background and objectives: Region based approach to determine the used land details of Vellore district to improvise the vegetation in future. Methods and Statistical Analysis: To classify the used lands Modified KNN and Modified SVM algorithm are applied to get the classification. Accuracy is measured by producer’s accuracy, user’s accuracy, commission error and omission error. Results: MOKNN and MOSVM produce the improved accuracy in classification to determine the exact land use details. Conclusion: Proposed MOKNN and MOSVM give the improved accuracy to predict used land details. This work may be extended with combined data set to get more improved accuracy.

Keywords

Classification, Fuzzy, KNN, MOKNN, MOSVM, SVM
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  • Enhanced Classification Algorithms for the Satellite Image Processing

Abstract Views: 198  |  PDF Views: 0

Authors

N. Suresh Kumar
School of Computing Science and Engineering, VIT University, Vellore - 632014, India
M. Arun
School of Electronics and Communication Engineering, VIT University, Vellore - 632014, India

Abstract


Background and objectives: Region based approach to determine the used land details of Vellore district to improvise the vegetation in future. Methods and Statistical Analysis: To classify the used lands Modified KNN and Modified SVM algorithm are applied to get the classification. Accuracy is measured by producer’s accuracy, user’s accuracy, commission error and omission error. Results: MOKNN and MOSVM produce the improved accuracy in classification to determine the exact land use details. Conclusion: Proposed MOKNN and MOSVM give the improved accuracy to predict used land details. This work may be extended with combined data set to get more improved accuracy.

Keywords


Classification, Fuzzy, KNN, MOKNN, MOSVM, SVM



DOI: https://doi.org/10.17485/ijst%2F2015%2Fv8i15%2F75317