Illumination and atmospheric conditions invariant transform for object detection in hyperspectral images

This paper presents a new procedure to make the at-sensor radiance comparable with the object reflectance spectrum in order to perform object detection and identification. A new transformation is proposed to account for the non-perfect knowledge of illumination, viewing and atmospheric conditions. The transformation, which is applied to both the image data and the object spectrum, takes into account the uncertainty of the acquisition conditions by resorting to a subspace based approach. The subspace is obtained by using a parametric physics-based radiative transfer model and by resorting to the MODerate resolution TRANmission (MODTRAN) radiative transfer code. The procedure is entailed in a target detection and identification scheme and is applied to real hyperspectral data acquired during a recently performed measurement campaign.