Efficient Algorithms for Atmospheric Correction of Remotely Sensed Data

Abstract

Remotely sensed imagery has been used for developing and validating vairous studies regarding land cover dynamics such as global carbon modeling, biogeochemical cycling, hydrological modeling, and ecosystem response modeling. However, the large amounts of imagery collected by the satellites are largely contaminated by the effects of atmospheric particles through absorption and scattering of the radiation from the earth surface. The objective of atmospheric correction is to retrieve the surface reflectance (that characterizes the surface properties) from remotely sensed imagery by removing the atmospheric effects. Atmospheric correction has been shown to significantly improve the accuracy of image classification. (Also cross-referenced as UMIACS-TR-95-53)