Hyperspectral image classification using spectral histograms and semi-supervised learning

In this paper, an algorithm that extracts regional texture information by computing spectral difference histograms over window extents in hyperspectral images is presented. The spectral angle distance is used as the spectral metric and different window sizes are explored for computing the histogram. The histograms are used in a semi-supervised learning framework that uses both labeled and unlabeled samples for training the support vector machine classifier, which is then tested with unlabeled samples. Results are presented with real and synthetic hyperspectral images. The method performs well with high spatial resolution images. The algorithm performs well under different noise levels.