Goel, P.K. ; Prasher, S.O. ; Landry, J.A. ; Patel, R.M. ; Viau, A.A. 2003. Hyperspectral image classification to detect weed infestations and nitrogen status in corn. Transactions of the ASAE. Vol. 46(2): 539–550
Abstract
The potential of hyperspectral aerial imagery for the detection of weed infestation and nitrogen fertilization level in a corn (Zea mays L.) crop was evaluated. A Compact Airborne Spectrographic Imager (CASI) was used to acquire hyperspectral data over a field experiment laid out at the Lods Agronomy Research Centre of Macdonald Campus, McGill University, Québec, Canada. Corn was grown under four weed management strategies (no weed control, control of grasses, control of broadleaf weeds, and full weed control) factorally combined with nitrogen fertilization rates of 60, 120, and 250 N kg/ha. The aerial image was acquired at the tasseling stage, which was 66 days after planting. For the classification of remote sensing imagery, various widely used supervised classification algorithms (maximum likelihood, minimum distance, Mahalanobis distance, parallelepiped, and binary coding) and more sophisticated classification approaches (spectral angle mapper and linear spectral unmixing) were investigated. It was difficult to distinguish the combined effect of both weed and nitrogen treatments simultaneously. However, higher classification accuracies were obtained when only one factor, either weed or nitrogen treatment, was considered. With different classifiers, depending on the factors considered for the classification, accuracies ranged from 65.84% to 99.46%. No single classifier was found useful for all the conditions.