Journal of Spectral Imaging, Volume 6 Article ID a2 (2017)

Peer reviewed paper

Multivariate data modelling for de-shadowing of airborne hyperspectral imaging

João F. Fortunaa,* and Harald Martensb,c
aCentre for Autonomous Marine Operations and Systems (NTNU AMOS), Department of Engineering Cybernetics, Norwegian University of Science and Technology (NTNU), Trondheim, Norway. E-mail: [email protected]
bDepartment of Engineering Cybernetics, NTNU, Trondheim, Norway
cIdletechs AS, Trondheim, Norway

Airborne hyperspectral imaging is a powerful technique for high-resolution classification of large areas of ground, applied today in fields like agriculture and environmental monitoring. Even though many classification algorithms are capable of handling shadows without a decrease in performance, visual inspection can be made easier if shadows are removed. In this paper we present a method for separating the effect of shadows (de-shadowing) and other partially known lighting condition changes from the effects due to the physical, chemical or biological properties of the ground, which are of interest. An example application is shown with good results.

Keywords: airborne, UAV, hyperspectral, de-shadowing, multivariate

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