INVESTIGADORES
BENTIVEGNA Diego Javier
artículos
Título:
Comparing multispectral and hyperspectral classifiers for mapping cut-leaved teasel in highway environments.
Autor/es:
WANG C; BENTIVEGNA D.J.; SMEDA R.J.; SWANIGAN R.E.
Revista:
PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING
Editorial:
AMER SOC PHOTOGRAMMETRY
Referencias:
Año: 2010 vol. 76 p. 567 - 575
ISSN:
0099-1112
Resumen:
Cut-leaved teasel is an invasive weed thriving in roadside environments and needs to be detected for implementation of management programs. This study tested several commonly applied classifiers to map teasel with an aerial hyperspectral image along the Interstate Highway 70 in central Missouri. A teasel/non-teasel mask was first built to exclude dominant land-covers that had distinct spectral differences from teasel. The spectral angle mapping (SAM) had the best results of delineating teasel from herbaceous background with its user’s and producer’s accuracies of 80 to 90 percent. Large commission errors of teasel were observed in the probability-based maximum likelihood classifier (MLC) and spectral information divergence (SID) methods. Compare with a regular land-use/land-cover classification in an unsupervised/supervised hybrid method, the post-masking SAM had much easier process of training data collection and achieved similar accuracies. It could be an optimal approach for mapping teasel and other weeds in highway environments.