Deep Learning for Classification of Hyperspectral Data: A Comparative Review
It targets data scientists and remote sensing experts by offering a comparative review and tools for applying deep learning to hyperspectral classification, but it is incremental as it synthesizes existing methods rather than introducing new ones.
This article reviews deep learning approaches for hyperspectral data classification, addressing specific challenges like spatial and spectral resolution, and provides a comparative study of network architectures along with a publicly released software toolbox.
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.