Accuracy assessment of hyperspectral imagery: atmospheric calibration and image classification considerations

Accuracy assessment is one of the most important considerations in the evaluation of remotely sensed imagery. Too often, it is not done when imagery is produced. The accuracy of an image is effected by many variables, including the spatial and spectral resolution of the hyperspectral sensor, processing statistics used, types of classifications chosen, limits of detection of different surface materials, suitability of reference spectra used for image analysis training, the type and amount of ground truth data acquisition, and type of atmospheric correction algorithm applied to the imagery. This presentation will discuss selected examples generated from work performed under the NASA EOCAP (Earth Observations Commercial Applications Program) project NAS 13-99004. The first example is from the Ray copper mine in Arizona, USA. It demonstrates the affects of spectral library references vs in situ ground truth, and different processing techniques on the identification and distribution of a target mineral, jarosite, in an image. The second example shows how the choice of processing cutoffs can change the distribution of a target mineral, alunite, in the image. The third example evaluates old and new atmospheric correction algorithms