  Undirected graphical models have been successfully used to jointly model the
spatial and the spectral dependencies in earth observing hyperspectral images.
They produce less noisy, smooth, and spatially coherent land cover maps and
give top accuracies on many datasets. Moreover, they can easily be combined
with other state-of-the-art approaches, such as deep learning. This has made
them an essential tool for remote sensing researchers and practitioners.
However, graphical models have not been easily accessible to the larger remote
sensing community as they are not discussed in standard remote sensing
textbooks and not included in the popular remote sensing software and
toolboxes. In this tutorial, we provide a theoretical introduction to Markov
random fields and conditional random fields based spatial-spectral
classification for land cover mapping along with a detailed step-by-step
practical guide on applying these methods using freely available software.
Furthermore, the discussed methods are benchmarked on four public hyperspectral
datasets for a fair comparison among themselves and easy comparison with the
vast number of methods in literature which use the same datasets. The source
code necessary to reproduce all the results in the paper is published on-line
to make it easier for the readers to apply these techniques to different remote
sensing problems.
