Symbolic representation of hyperspectral data

We have developed a symbolic representation of hyperspectral data using the scale space techniques of Witkin. We created a scale space image of hyperspectral data from convolution with Gaussian masks and then a fingerprint that extracts individual features from the original data. The fingerprint provides a context that pairs inflection points and assigns them to a feature, generates a measure of importance for each feature, and relates features to each other. The representation is an ordered sequence of triplets containing a measure of importance related to the area of each feature and the left and right inflection points of the feature. The description is compact, quantitative, and hierarchical, describing the hyperspectral curve by its most important structural features first, followed by features of lesser importance.