Multivariate data modelling for de-shadowing of airborne hyperspectral imaging
Journal article, Peer reviewed
Åpne
Permanent lenke
http://hdl.handle.net/11250/2432224Utgivelsesdato
2017-02Metadata
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Originalversjon
10.1255/jsi.2017.a2Sammendrag
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.