  In recent years, deep learning techniques revolutionized the way remote
sensing data are processed. Classification of hyperspectral data is no
exception to the rule, but has intrinsic specificities which make application
of deep learning less straightforward than with other optical data. This
article presents a state of the art of previous machine learning approaches,
reviews the various deep learning approaches currently proposed for
hyperspectral classification, and identifies the problems and difficulties
which arise to implement deep neural networks for this task. In particular, the
issues of spatial and spectral resolution, data volume, and transfer of models
from multimedia images to hyperspectral data are addressed. Additionally, a
comparative study of various families of network architectures is provided and
a software toolbox is publicly released to allow experimenting with these
methods. 1 This article is intended for both data scientists with interest in
hyperspectral data and remote sensing experts eager to apply deep learning
techniques to their own dataset.
