Support vector selection and adaptation for remote sensing classification

Gülşen Taşkin Kaya*, Okan K. Ersoy, Mustafa E. Kamaşak

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

23 Citations (Scopus)


Classification of nonlinearly separable data by nonlinear support vector machines (SVMs) is often a difficult task, particularly due to the necessity of choosing a convenient kernel type. Moreover, in order to get the optimum classification performance with the nonlinear SVM, a kernel and its parameters should be determined in advance. In this paper, we propose a new classification method called support vector selection and adaptation (SVSA) which is applicable to both linearly and nonlinearly separable data without choosing any kernel type. The method consists of two steps: selection and adaptation. In the selection step, first, the support vectors are obtained by a linear SVM. Then, these support vectors are classified by using the $K$-nearest neighbor method, and some of them are rejected if they are misclassified. In the adaptation step, the remaining support vectors are iteratively adapted with respect to the training data to generate the reference vectors. Afterward, classification of the test data is carried out by 1-nearest neighbor with the reference vectors. The SVSA method was applied to some synthetic data, multisource Colorado data, post-earthquake remote sensing data, and hyperspectral data. The experimental results showed that the SVSA is competitive with the traditional SVM with both linearly and nonlinearly separable data.

Original languageEnglish
Article number5682043
Pages (from-to)2071-2079
Number of pages9
JournalIEEE Transactions on Geoscience and Remote Sensing
Issue number6 PART 1
Publication statusPublished - Jun 2011


Manuscript received November 10, 2009; revised March 13, 2010, October 5, 2010, and November 20, 2010; accepted November 20, 2010. Date of publication January 5, 2011; date of current version May 20, 2011. This work was supported by the Scientific and Technological Research Council of Turkey (TUBITAK).

FundersFunder number
Türkiye Bilimsel ve Teknolojik Araştirma Kurumu


    • Classification of multisource
    • hyperspectral and multispectral images
    • support vector machines (SVMs)
    • support vector selection and adaptation (SVSA)


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