  In this paper, we propose and compare two spectral angle based approaches for
spatial-spectral classification. Our methods use the spectral angle to generate
unary energies in a grid-structured Markov random field defined over the pixel
labels of a hyperspectral image. The first approach is to use the exponential
spectral angle mapper (ESAM) kernel/covariance function, a spectral angle based
function, with the support vector machine and the Gaussian process classifier.
The second approach is to directly use the minimum spectral angle between the
test pixel and the training pixels as the unary energy. We compare the proposed
methods with the state-of-the-art Markov random field methods that use support
vector machines and Gaussian processes with squared exponential
kernel/covariance function. In our experiments with two datasets, it is seen
that using minimum spectral angle as unary energy produces better or comparable
results to the existing methods at a smaller running time.
