Empirical versus Model‐based Atmospheric Correction of Digital Airborne Imaging Spectrometer Hyperspectral Data

Abstract

Abstract This paper reports the results of a quantitative comparison of empirical and model based atmospheric correction techniques for the radiometric calibration of a Digital Airborne Imaging Spectrometer (DAIS) 3715 hyperspectral image dataset. Empirical line calibration (ELC) and the radiative transfer based model Atmospheric CORrection Now (ACORN) were applied to transform the hyperspectral dataset from values of radiance to scaled percent reflectance. An additional spectral polishing technique called single spectrum enhancement (SSE) was implemented a posteriori to refine the transformation results. To evaluate the accuracy of the radiometric calibration techniques, spectra extracted from the processed images were analytically compared to spectral measurements collected in situ with a handheld spectroradiometer at 46 sample point locations. Average RMSE errors were as follows: ELC without SSE = 0.1415, ACORN without SSE = 0.0645, ELC with SSE = 0.0345, and ACORN with SSE = 0.0314. Based on the results of this analysis, spectral polishing through the use of SSE appears to introduce the greatest improvement in the removal of deleterious atmospheric effects when compared to in situ data, regardless of the choice of the model (i.e. ACORN or ELC).