Conference on Intelligent Data Understanding 2010 (CIDU)

SPATIALLY ADAPTIVE SEMI-SUPERVISED LEARNING WITH GAUSSIAN
PROCESSES FOR HYPERSPECTRAL DATA ANALYSIS
GOO JUN * AND JOYDEEP GHOSH*

Abstract. A semi-supervised learning algorithm for the classification of hyperspectral data,
Gaussian process expectation maximization (GP-EM), is proposed. Model parameters for each
land cover class is first estimated by a supervised algorithm using Gaussian process regressions
to find spatially adaptive parameters, and the estimated parameters are then used to initialize a
spatially adaptive mixture-of-Gaussians model. The mixture model is updated by expectationmaximization
iterations using the unlabeled data, and the spatially adaptive parameters for unlabeled
instances are obtained by Gaussian process regressions with soft assignments. Two sets
of hyperspectral data taken from the Botswana area by the NASA EO-1 satellite are used for experiments.
Empirical evaluations show that the proposed framework performs significantly better
than baseline algorithms that do not use spatial information, and the results are also better than
any previously reported results by other algorithms on the same data.