Classification of Contamination in Salt Marsh Plants Using Hyperspectral Reflectance
Machelle D. Wilson, Susan L. Ustin, Member; IEEE, and David M.
Rocke
Abstract-In this paper, we compare the classification
effectiveness of two relatively new techniques on data consisting of leaf-level
reflectance from five species of salt marsh and two species of crop plants
(in four experiments) that have been exposed to varying levels of different
heavy metal or petroleum toxicity, with a control treatment for each experiment.
If these methodologies work well on leaf-level data, then there is hope
that they will also work well on data from air- and spaceborne platforms.
The classification methods compared were support vector classification
(SVC) of exposed and nonexposed plants based on the spectral reflectance
data, and partial least squares compression of the spectral reflectance
data followed by classification using logistic discrimination (PLS/LD).
The statistic we used to compare the effectiveness of the methodologies
was the leave-one-out cross-validation estimate of the prediction error.
Our results suggest that both techniques perform reasonably well, but
that SVC was superior to PLS/LD for use on hyperspectral data and it is
worth exploring as a technique for classifying heavy-metal or petroleum
expose plants for the more complicated data from air- and space borne
sensors.
Index Terms-Heavy metals, hyperspectral, logistic discrimination
(LD), partial least squares (PLS), petroleum, reflectance, remote sensing,
support vector machines (SVMs).
SREL Reprint #2768
Wilson, M. D., S. L. Ustin and D. M. Rocke. 2004. Classification of contamination in salt marsh plants using hyperspectral reflectance. IEEE Transactions on Geoscience and Remote Sensing 42:1088-1095.
