Estimating atmosphere parameters in hyperspectral data

摘要

We address the problem of estimating atmosphere parameters (temperature, water vapour content) from datacaptured by an airborne thermal hyperspectral imager, and propose a method based on direct optimization. Themethod also involves the estimation of object parameters (temperature and emissivity) under the restriction thatthe emissivity is constant for all wavelengths. Certain sensor parameters can be estimated as well in the sameprocess. The method is analyzed with respect to sensitivity to noise and number of spectral bands. Simulationswith synthetic signatures are performed to validate the analysis, showing that estimation can be performed withas few as 10-20 spectral bands at moderate noise levels. More than 20 bands does not improve the estimates. Theproposed method is also extended to incorporate additional knowledge, for example measurements of atmosphericparameters and sensor noise.