Compensation of Hyperspectral Data for Atmospheric Effects

■ Hyperspectral imaging sensors are used to detect and identify diverse surface materials, topographical features, and geological features. Because the intervening atmosphere poses an obstacle to the retrieval of surface reflectance data, algorithms exist to compensate the measured signal for the effects of the atmosphere. This article provides an overview and an evaluation of available atmospheric compensation algorithms for the visible-through-shortwave infrared spectral region, including comparison of operational characteristics, input requirements, algorithm limitations, and computational requirements. Statistical models based on empirical in-scene data are contrasted with physicsbased radiative transfer algorithms. The statistical models rely on a priori scene information that is coupled with the sensor spectral observations in a regression algorithm. The physics-based models utilize physical characteristics of the atmosphere to derive water vapor, aerosol, and mixed gas contributions to the atmospheric signal. Treatment of aerosols in atmospheric compensation models varies considerably and is discussed in some detail. A three-band ratio approach is generally used for the retrieval of atmospheric water vapor. For the surfaces tested in this study, the retrieved surface reflectances from the two physics-based algorithms are similar under dry, clear conditions but differ under moist, hazy conditions. Sensitivity of surface-reflectance retrievals to variations in scene characteristics such as the solar zenith angle, atmospheric visibility, aerosol type, and the atmospheric temperature profile is presented in an effort to quantify the limitations of the models.