With the greater availability of imaging spectrometer data, vegetation species classification in the presence of outlier and ambiguous spectra is an increasingly important and poorly addressed problem. At large scales, assuming that all test spectra are from one of the training classes is unrealistic. An attractive resolution of these problems is the possibility theory, which is an axiomatic system, like probability theory, that represents uncertain labels of outliers and ambiguities more flexibly. In this letter, two popular probabilistic classification algorithms, namely, the support vector machine (SVM) with Platt scaling (SVM-Platt) and the Gaussian process (GP) classifier (GPC), are evaluated and compared to a novel one-class GP (OCGP) possibilistic classifier. OCGP, unlike one-class classification with GP, another GP-based one-class classifier, finds the hyperparameters automatically. Experiments were conducted with two data sets. The OCGP outperformed SVM-Platt and GPC when tested with outlier spectra.
['Leila Kalantari', 'Paul D. Gader', 'Sarah J. Graves', 'Stephanie A. Bohlman']
One-Class Gaussian Process for Possibilistic Classification Using Imaging Spectroscopy