Graph-based semi-supervised learning for spectral-spatial hyperspectral image classification

Abstract

Spectral and spatial regularized semi-supervised learning is investigated for classification of hyperspectral images. In spectral regularizer, sum of minimum distance (SMD) is introduced to determine graph adjacency relationships and local manifold learning (LML) is employed for edge weighting. The resulted SMD_LML regularizer is able to constrain the prediction vectors to preserve the local geometry of each neighborhood. In spatial regularizer, spatial neighbors with similar spectra are enforced to have similar predictions. The two regularizers capture different relations of data points and play complementary roles. By combining SMD_LML regularizer and spatial regularizer in graph based semi-supervised learning, the local properties of both spectral neighborhood and spatial neighborhood can be preserved in the prediction domain. Experiments with AVIRIS and ROSIS hyperspectral images demonstrated that SMD can produce more accurate adjacency relations than the other two popular distance measurements. The classification with SMD_LML and spatial regularizer achieved significant improvements than several spectral and spatial based methods.