# Deep Learning for Classification of Hyperspectral Data: A Comparative Review

## TL;DR

This paper reviews the application of deep learning techniques to hyperspectral data classification, comparing various architectures, discussing challenges, and providing a software toolbox for experimentation.

## Contribution

It offers a comprehensive comparison of deep learning methods for hyperspectral classification and addresses implementation challenges specific to hyperspectral data.

## Key findings

- Comparison of different neural network architectures
- Identification of key challenges in hyperspectral deep learning
- Provision of an open-source toolbox for experimentation

## Abstract

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.