期刊
ADVANCES IN SPACE RESEARCH
卷 41, 期 11, 页码 1793-1799出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.asr.2008.02.012
关键词
primal Support Vector Machine (SVM); classification; small-size training dataset problem; hyperspectral remote-sensing data
With recent technological advances in remote sensing, very high-dimensional (hyperspectral) data are available for a better discrimination among different complex land-cover classes having similar spectral signatures. However, this large number of bands makes very complex the task of automatic data analysis. In the real application, it is difficult and expensive for the expert to acquire enough training samples to learn a classifier. This results in a classification problem with small-size training sample set. Recently, a regularization-based algorithm is usually proposed to handle such problem, such as Support Vector Machine (SVM), which usually are implemented in the dual form with Lagrange theory. However, it can be solved directly in primal formulation. In this paper, we introduces an alternative implementation technique for SVM to address the classification problem with small-size training sample set. It has been empirically proven that the effectiveness of the introduced implementation technique which has been evaluated by benchmark datasets. (c) 2008 COSPAR. Published by Elsevier Ltd. All rights reserved.
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