A hybrid entropy decomposition and support vector machine method for agricultural crop type classification

Tan, Chue-Poh and Ewe, Hong-Tat and Chuah, Hean-Teik (2007) A hybrid entropy decomposition and support vector machine method for agricultural crop type classification. In: Progress in Electromagnetics Research Symposium (PIERS 2007) , 26-30 MAR 2007, Beijing, PEOPLES R CHINA.

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Abstract

This paper presents the development of Synthetic Aperture Radar (SAR) image classifier based on the hybrid method of "Entropy Decomposition and Support Vector Machine" (EDSVM) for agricultural crop type classification. The Support Vector Machine (SVM) is successfully applied to the key parameters extracted from Entropy Decomposition to obtain good image classifications. In this paper, this novel classifier has been applied on a multi-crop region of Flevoland, Netherlands with multi-polarization data for crop type classification. Validation of the classifiers has been carried out by comparing the classified image obtained from EDSVM classifier and SVM. The EDSVM classifier demonstrates the advantages of the valuable decomposed parameters and statistical machine learning theory in performing better results compared with the SVM classifier. The final outcome of this research clearly indicates that EDSVM has the ability in improving the classification accuracy for agricultural crop type classification.

Item Type: Conference or Workshop Item (Paper)
Subjects: T Technology > T Technology (General)
Q Science > QA Mathematics > QA75.5-76.95 Electronic computers. Computer science
Divisions: Faculty of Engineering and Technology (FET)
Depositing User: Ms Suzilawati Abu Samah
Date Deposited: 13 Oct 2011 08:28
Last Modified: 13 Oct 2011 08:28
URI: http://shdl.mmu.edu.my/id/eprint/3238

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