Machelle Wilson
Savannah River Ecology Laboratory
Support Vector Classification of Hyper-Spectral Data on Plants Exposed
to Metal Toxicity
Remote sensing technologies with high spatial and spectral resolution show a
great deal of promise in addressing critical environmental monitoring
issues, but the ability to analyze and interpret the data lags behind the
technology. Robust analytical methods are required before the wealth of data available
through remote sensing can be applied to a wide range of environmental
problems for which remote detection is the best method. In this talk, I introduce the basic theory behind support vector machines
(SVM) and discuss their effectiveness in classifying plants that have been exposed
(or not) to varying levels of heavy metal toxicity using leaf-level hyperspectral (reflectance) data. If SVM work well on leaf-level data, then
there is some hope that they will also work well on data from airborne and
space-borne platforms, which are significantly more complicated. We discuss
the probabilistically derived bounds on the prediction error, as well as
leave-one-out cross validation to assess the effectiveness of SVMs on two data
sets from a controlled greenhouse study.
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