Thematic labelling from hyperspectral remotely sensed imagery: trade-offs in image properties

Abstract

The effect of spatial, spectral and noise degradations on the accuracy of two highly contrasting thematic labelling scenarios was investigated. The study used hyperspectral imagery of a site near Falmouth, UK, to assess the effect of the data degradations on the accuracy of supervised classification when the H-resolution scene model was applicable and on labelling when an L-resolution scene model was applicable and no ground data were available. In both scenarios, the spatial, spectral and noise degradations affected the accuracy of labelling. However, over the range of degradations investigated, the noise content of the data was consistently noted to be a major variable affecting the accuracy of labelling