Evaluating the performance of a new classifier  the GP-OAD: A comparison with existing methods for classifying rock type and mineralogy from hyperspectral imagery
Gaussian Processes incorporating SAM at core for hyperspectral data classification. Systematic comparison of machine-learning methods under various conditions. Data acquired from different materials, sensors and different illumination. Classification using such independent training and test data sets. The presented method provides a Bayesian framework as basis for data fusion.
classification hyperspectral absorption feature iron minerals vertical geology illumination conditions machine learning remote sensing