期刊名称：ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
印刷版ISSN：2194-9042
电子版ISSN：2194-9050
出版年度：2013
卷号：XL-1/W3
页码：209-214
DOI：10.5194/isprsarchives-XL-1-W3-209-2013
出版社：Copernicus Publications
摘要：Supervised classification of hyperspectral images is a difficult task due to the unbalance between the high dimensionality of the data and the limited availability of labeled training samples. Recently support vector machine (SVM), has received considerable attention for classifying high dimensional data and is applied successfully for classification of hyperspectral images because it discriminates classes by a geometrical criterion not by statistical criteria. In this paper, we investigate sensitivity of SVM classifier respect to two factors. The first factor is the dimensional of data (the number of features) and the second factor is the number of training samples. We evaluate the effect of these factors on the performance of classification in the point of view both accuracy and reliability. Experiments are carried out on the three different common used hyperspectral datasets, Indian pines, Pavia University and Salinas
关键词：Support vector machine; Hyperspectral images; the number of features; the number of training samples
