Spatially adaptive semi-supervised learning with Gaussian processes for hyperspectral data analysis



This paper presents a semi-supervised learning algorithm called Gaussian process expectation-maximization (GP-EM), for classification of landcover based on hyperspectral data analysis. Model parameters for each land cover class are 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 expectation-maximization iterations using the unlabeled data, and the spatially adaptive parameters for unlabeled instances are obtained by Gaussian process regressions with soft assignments. Spatially and temporally distant hyperspectral images taken from the Botswana area by the NASA EO-1 satellite are used for experiments. Detailed empirical evaluations show that the proposed framework performs significantly better than all previously reported results by a wide variety of alternative approaches and algorithms on the same datasets. © 2011 Wiley Periodicals, Inc. Statistical Analysis and Data Mining 4: 358–371, 2011