Hyperspectral images which are captured in narrow bands in continuous manner contain very large data. This data need high processing power to classify and may contain redundant information. A variety of dimension reduction methods are used to cope with this high dimensionality. In this paper, the effect of sub-sampling hyperspectral images for dimension reduction techniques is explored and compared in classification performance and calculation time. © Springer International Publishing Switzerland 2013.
CITATION STYLE
Kozal, A. Ö., Teke, M., & Ilgin, H. A. (2013). The Effect of Sub-sampling on Hyperspectral Dimension Reduction. Advances in Intelligent Systems and Computing, 210, 529–537. https://doi.org/10.1007/978-3-319-00542-3_52
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