A VNIR/SWIR atmospheric correction algorithm for hyperspectral imagery with adjacency effect

Abstract

Radiometrically calibrated hyperspectral imagery contains information relating to the material properties of a surface target and the atmospheric layers between the surface target and the sensor. All atmospheric layers contain well-mixed molecular gases, aerosol particles, and water vapor, and information about these constituents may be extracted from hyperspectral imagery by using specially designed algorithms. This research describes a total sensor radiance-to-ground reflectance inversion program. An equivalent surface-pressure depth can be extracted using the Non-Linear Least-Squares Spectral Fit (NLLSSF) technique on the 760-nm oxygen band. Two different methods, the Atmospheric Pre-Corrected Differential Absorption (APDA) and NLLSSF, can be used to derive total columnar water vapor using the radiative transfer model MODTRAN 4.0. Atmospheric visibility can be derived via the NLLSSF technique from the 400–700-nm bands or using an approach that uses the upwelled radiance fit from the Regression Intersection Method from 550 to 700 nm. A new numerical approximation technique is also introduced to calculate the effect of the target surround on the sensor-received radiance. The recovered spectral reflectances for each technique are compared to reflectance panels with well-characterized ground truth.