Evaluating laboratory-based classification potentials of heavy metal contaminated soils using spectro-radiometer and hyper-spectral imagery

Abstract

In this study, we constructed indoor testbed to apply aerial hyper-spectral imaging sensor was to monitoring to heavy metal contaminated in soil. And a ground-based spectro-radiometer data and hyper-spectral image data were obtained for soil samples artificially contaminated with two heavy metals (Cu, Pb). Based on the comparison of spectro-radiometer and actual concentration data, specific wavelength band was classified by linear regression analysis and statistical analysis. We have established a preprocessing methodology that represents the most correlated specific wavelengths through logarithmic and derivative transformations on the reflectance data for each concentration gradient of heavy metals. Among the various methods, (Log (1/R))′ conversion was confirmed as the most effective pre-processing method for extracting specific wavelength. Then, the spectral characteristic patterns of the soil contaminated with heavy metals were confirmed through the hyper-spectral image data constructed in the indoor test bed, and spectral angle mapper which is one of the spectral characteristic matching methods was applied to confirm the classification feasibility of soil contaminated with the heavy metals.