dc.contributor.author | Fortuna, João | |
dc.contributor.author | Martens, Harald | |
dc.date.accessioned | 2017-02-27T13:40:58Z | |
dc.date.available | 2017-02-27T13:40:58Z | |
dc.date.created | 2017-02-20T13:58:34Z | |
dc.date.issued | 2017-02 | |
dc.identifier.issn | 2040-4565 | |
dc.identifier.uri | http://hdl.handle.net/11250/2432224 | |
dc.description.abstract | 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. | nb_NO |
dc.language.iso | eng | nb_NO |
dc.relation.uri | https://doi.org/10.1255/jsi.2017.a2 | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/deed.no | * |
dc.title | Multivariate data modelling for de-shadowing of airborne hyperspectral imaging | nb_NO |
dc.type | Journal article | nb_NO |
dc.type | Peer reviewed | nb_NO |
dc.source.volume | 6 | nb_NO |
dc.source.journal | Journal of Spectral Imaging | nb_NO |
dc.identifier.doi | 10.1255/jsi.2017.a2 | |
dc.identifier.cristin | 1452335 | |
dc.description.localcode | © 2017 The Authors. This licence permits you to use, share, copy and redistribute the paper in any medium or any format provided that a full citation to the original paper in this journal is given, the use is not for commercial purposes and the paper is not changed in any way | nb_NO |
cristin.unitcode | 194,63,25,0 | |
cristin.unitname | Institutt for teknisk kybernetikk | |
cristin.ispublished | false | |
cristin.fulltext | original | |
cristin.qualitycode | 1 | |