Support vector machines classification for finding building patches from IKONOS imagery: the effect of additional bands

Dilek Koc San; Mustafa Turker

doi:10.1117/1.JRS.8.083694

10 January 2014 Support vector machines classification for finding building patches from IKONOS imagery: the effect of additional bands

Dilek Koc San, Mustafa Turker

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 8, Issue 1, 083694 (January 2014). https://doi.org/10.1117/1.JRS.8.083694

Abstract

This study aims to find building patches from pan-sharpened IKONOS imagery using two-class support vector machines (SVM) classification. In addition to original bands of the image, the normalized digital surface model, normalized difference vegetation index, and several texture measures (mean, variance, homogeneity, contrast, dissimilarity, entropy, second moment, and correlation) are also used in the classification. The study illustrates the performance of the binary SVM classification in building detection from IKONOS imagery. Moreover, the effect of additional bands in building detection is examined. The approach was tested in three test sites that are located in the Batikent district of Ankara, Turkey. The SVM classification provided quite accurate results with the building detection percentage (BDP) values in the range 81.27–96.26% and the quality percentage (QP) values in the range 41.01–74.83%. It was found that the usage of additional bands in SVM classification had a significant effect in building detection accuracy. When compared to results obtained using solely the original bands, the additional bands increased the accuracy up to 10.44% and 8.45% for BDP and QP, respectively.

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Dilek Koc San and Mustafa Turker "Support vector machines classification for finding building patches from IKONOS imagery: the effect of additional bands," Journal of Applied Remote Sensing 8(1), 083694 (10 January 2014). https://doi.org/10.1117/1.JRS.8.083694

Published: 10 January 2014

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