Adaptive Classification of Hyperspectral Image

important problem in pattern recognition is the effect of limited training samples on classification performance. When the ratio of the number of training samples to the dimensionality is small, parameter estimates become highly variable, causing the deterioration of classification performance. This problem has become more prevalent in remote sensing with the emergence of a new generation of sensors While the new sensor technology provides higher spectral and spatial resolution, enabling a greater number of spectrally separable classes to be identified the needed labeled samples for designing the classifier remain difficult and expensive to acquire

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