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 NLLSSF technique on the 760nm oxygen band. Two different methods (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 400-700nm bands or using a new approach that uses the upwelled radiance fit from the Regression Intersection Method from 550nm- 700nm. 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.
Department, Program, or Center
Chester F. Carlson Center for Imaging Science (COS)
Sanders, Lee, "An Atmospheric correction algorithm for hyperspectral imagery" (2011). Thesis. Rochester Institute of Technology. Accessed from
RIT – Main Campus