A sensitivity assessment of terrestrial identification in remote sensing
Correcting for atmospheric effects is an essential part of surface-reflectance recovery from radiance measurements. Model-based atmospheric correction techniques improve the accuracy of the identification and classification of terrestrial reflectances from multi-spectral imagery. Successful and efficient removal of atmospheric effects from remote sensing data is a key factor in the success of Earth observation missions. This paper assesses the performance, robustness and sensitivity of Richter's atmospheric-correction technique as part of an end-to-end stochastic simulation of hyper-spectral acquisition, identification and classification.