Land cover classification from hyperspectral remotely sensed data: an investigation of spectral, spatial and noise issues

The effect of spatial, spectral and noise degradations on the accuracy of two thematic labelling scenarios with hyperspectral data was investigated. Although all of the degradations significantly influenced accuracy, the noise content of the data was consistently noted as a major variable affecting the accuracy of both supervised classification and sub-pixel anomaly detection analyses.