Classification of High Dimensional and Imbalanced Hyperspectral Imagery Data

Abstract

The present paper addresses the problem of the classification of hyperspectral images with multiple imbalanced classes and very high dimensionality. Class imbalance is handled by resampling the data set, whereas PCA is applied to reduce the number of spectral bands. This is a preliminary study that pursues to investigate the benefits of using together these… (More)
DOI: 10.1007/978-3-642-21257-4_80

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