Universiti Teknologi Malaysia Institutional Repository

Integration of spectral information into support vector machine for land cover classification

Md. Sap, Mohd. Noor and Kohram, Mojtaba (2007) Integration of spectral information into support vector machine for land cover classification. Jurnal Teknologi Maklumat, 19 (2). pp. 47-56. ISSN 0128-3790

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Support vector machines (SVM) have been widely used for classification purposes. These learning machines are based on classification of data through a kernel function. Classically these kernel functions are either based the Euclidean distance of two data vectors or their dot products. This is a general formulation which is suitable for most data sets. However, when dealing with remote sensing images, the addition of spectral information can add to the divisibility of the data and hence produce higher classification accuracy. In this paper, instead of the Euclidean distance we use the spectral angle function as a differentiation measure of two data vectors. The results show that using this method, high quality separation is achieved leading us to believe that integration of spectral information into the SVM method is indeed an effective approach.

Item Type:Article
Uncontrolled Keywords:support vector machines, kernels, spectral angle, classification, land cover
Subjects:Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4050 Electronic information resources
Divisions:Computer Science and Information System
ID Code:8184
Deposited By: Norshiela Buyamin
Deposited On:02 Apr 2009 06:21
Last Modified:01 Nov 2017 04:17

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