Comparison of support vector machine classification to partial least squares dimension reduction with logistic discrimination of hyperspectral data

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 study we compare the classification effectiveness of two relatively new techniques on data consisting of leaf-level reflectance from plants that have been exposed to varying levels of heavy metal toxicity. If these methodologies 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. The classification methods compared were support vector machine (SVM) classification of exposed and nonexposed plants based on the reflectance data, and partial least squares compression of the reflectance data followed by classification using logistic discrimination (PLS/LD). PLS/LD was performed in two ways. We used the continuous concentration data as the response during compression, and then used the binary response required during logistic discrimination. We also used a binary response (-1 for non-exposed, and 1 for exposed) during compression followed by logistic discrimination. The statistic we used to compare the effectiveness of the methodologies was the leave-one-out cross validation estimate of the prediction error. PLS/LD using binary predictor variables during compression had the lowest estimated prediction error for the data analyzed here.