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Airborne multispectral imagers have been used in validation campaigns in order to acquire very high spatial resolution data as a benchmark for current or future satellite data. Imagery acquired with such sensors implies specific data processing in relation to view-angle-dependent atmospheric correction and removal or minimization of stripping-based noise. It is necessary to appropriately perform this processing in order to benefit from reference imageries of surface temperature (T) and emissivity (ε) maps retrieved from thermal infrared data. In particular, ε images generated from T/ε separation algorithms show undesirable noise that jeopardizes their photointerpretation. This letter addresses the following: 1) the removal of view-angle-dependent atmospheric effects by using ratio techniques for deriving atmospheric water vapor content in a pixel-by-pixel basis and atmospheric radiative transfer simulations to construct lookup tables (LUTs) and 2) the removal of image stripping using maximum/minimum noise fraction (MNF) transforms. For this purpose, imagery acquired with the Airborne Hyperspectral Scanner (AHS) sensor has been used. Results show that angular effects in the atmospheric correction can be addressed from AHS-derived water vapor content and LUTs, whereas due to the AHS noise specific characteristics, the MNF transform only removed part of the noise.