One-Class Gaussian Process for Possibilistic Classification Using Imaging Spectroscopy

• Published in: IEEE geoscience and remote sensing letters Vol. 13; no. 7; pp. 967 - 971
• Main Authors: Kalantari, Leila, Gader, Paul, Graves, Sarah, Bohlman, Stephanie A.
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.07.2016; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Ambiguity representation; Classification; Classifiers; Gaussian; Gaussian process (GP); Gaussian processes; Ground penetrating radar; hyperspectral (HS); Kernel; outliers; Outliers (statistics); Platt scaling; possibilistic classifiers; probabilistic classifiers; Probabilistic logic; Probability theory; species classifiers; Spectra; support vector machine (SVM); Support vector machines; Training; Gaussian process (GP); probabilistic classifiers; possibilistic classifiers; support vector machine (SVM); hyperspectral (HS); species classifiers; Ambiguity representation; outliers; Platt scaling
• ISSN: 1545-598X; 1558-0571
• DOI: 10.1109/LGRS.2016.2557315

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.