A comparative study of several supervised classifiers for coconut palm tree fields' type mapping on 80cm RGB pansharpened Ikonos images

R. Teina; D. Béréziat; B. Stoll; S. Chabrier

doi:10.1117/12.805736

2 February 2009 A comparative study of several supervised classifiers for coconut palm tree fields' type mapping on 80cm RGB pansharpened Ikonos images

R. Teina, D. Béréziat, B. Stoll, S. Chabrier

Author Affiliations +

Proceedings Volume 7251, Image Processing: Machine Vision Applications II; 72510X (2009) https://doi.org/10.1117/12.805736
Event: IS&T/SPIE Electronic Imaging, 2009, San Jose, California, United States

Abstract

The purpose of this study is to classify the types of coconut plantation. To this end, we compare several classifiers such as Maximum Likelihood, Minimum Distance, Parallelepiped, Mahalanobis and Support Vector Machines (SVM). The contribution of textural informations and spectral informations increases the separability of different classes and then increases the performance of classification algorithms. Before comparing these algorithms, the optimal windows size, on which the textural information are computed, as well as the SVM parameters are first estimated. Following this study, we conclude that SVM gives very satisfactory results for coconut field type mapping.

Citation Download Citation

R. Teina, D. Béréziat, B. Stoll, and S. Chabrier "A comparative study of several supervised classifiers for coconut palm tree fields' type mapping on 80cm RGB pansharpened Ikonos images", Proc. SPIE 7251, Image Processing: Machine Vision Applications II, 72510X (2 February 2009); https://doi.org/10.1117/12.805736

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