Existence of characteristic spectral signatures for agricultural crops – potential for automated crop mapping by hyperspectral imaging

Abstract

Image classification for material mapping using independent training data is emerging as an automated method for hyperspectral image analysis. Possibility of using independent training data for image classification depends upon material type and its spectral behaviour. Identification and spectral discrimination of materials which exhibit characteristic spectral behaviour are critical for developing hyperspectral material detection and mapping methods. We identify and evaluate characteristic reflectance signature of winter rape relative to its co-occurring crops from a hyperspectral image classification perspective. Spectrallibraries developed using field reflectance measurements of agricultural crops: alfalfa, winter barley, winter rape, winter rye and winter wheat collected during four growing seasons are searched through for the classification of a HyMap image acquired for a separate site by spectral angle mapper and spectralfeature fitting methods. Results indicate the existence of a characteristic spectral signature for winter rape and meaningful matching between image and field spectra, which can be used for automatic mapping of winter rape by hyperspectral imaging.