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Achieving reliable ground cover maps and high classification accuracies using limited ground truth is a key challenge for hyperspectral data analysts. In this paper, we explore the benefits of combining spectral derivative information along with reflectance information for hyperspectral classification. In addition to providing useful class-specific slope information, spectral derivatives are likely...
Hyperspectral signatures provide a dense recording of reflectance values over a wide region of the spectrum. This potentially increases the class separation capacity of the data as compared to gray scale imagery (where most of the class specific information is extracted from spatial relations between pixels) or multi-spectral imagery (where reflectance values at a few spectral bands are recorded)...
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