Gaussian Processes with OAD Covariance Function for Hyperspectral Data Classification

A new method is presented which combines a deterministic analytical method and a probabilistic measure to classify rock types on the basis of their hyperspectral curve shape. This method is a supervised learning algorithm using Gaussian Processes (GPs) and the Observation Angle Dependent (OAD) covariance function. The OAD covariance function makes use of the properties of the Spectral Angle Mapper (SAM) which is used frequently for classifying hyperspectral data. Results show that it is possible to identify and classify rocks in an `One vs. One´ and an `One vs. All´ approach using the entire spectral curve (0.35-2.5 μm). The results show an average classification accuracy of 98% and an F-score of 92% for the new method in an `One vs. All´ approach. Slightly higher classification accuracy and F-measure for the new method can be achieved for the `One vs. One´ binary approach. This paper extends the ideas of the deterministic SAM method to a probabilistic framework and enables data fusion with similar and disparate kinds of sensors. This paper demonstrates a superior classification performance of the new probabilistic method over the classical SAM.