Deep Learning for Classification of Hyperspectral Data: A Comparative Review

By Nicolas Audebert, Bertrand Le Saux, Sébastien Lefèvre. IEEE Geoscience and Remote Sensing Magazine, 2019

595 citations recorded by the source corpus.

In recent years, deep-learning techniques revolutionized the way remote sensing data are processed. The classification of hyperspectral data is no exception to the rule, but it has intrinsic specificities that make the application of deep learning less straightforward than with other optical data. This article presents the state of the art of previous machine-learning approaches, reviews the various deeplearning approaches currently proposed for hyperspectral classification, and identifies the problems and difficulties that arise in the implementation of 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 (https://github.com/nshaud/DeepHyperX). This article is intended for both data scientists with interest in hyperspectral data and remote sensing experts eager to apply deeplearning techniques to their own data set.

Subfields: Atmospheric Science, Ecology, Media Technology

View Paper: https://www.semanticscholar.org/paper/3e227ab8259f36757b2fc29f63db2f52f8e77ea2

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Citations in the corpus

A Review on Deep Learning in UAV Remote Sensing (2021, background): “Yet in the literature revision theme, a comparative review by Audebert et al. [8] was conducted by examining various families of networks’ architectures while providing a toolbox to perform such methods to be publicly available.”
Self Supervised Learning for Few Shot Hyperspectral Image Classification (2022, methodology): “We analyze the impact of data augmentation on the overall accuracy of Barlow-Twins on PaviaU with a linear classifier using the training map from [9].”
Spatial-Spectral Transformer for Hyperspectral Image Classification (2021, methodology): “A great many supervised methods have been proposed for HSI classification in the last two decades [11].”
Perspectives in machine learning for wildlife conservation (2021, background): “These extra bands are highly appreciated in plant ecology (90) and multi- and hyperspectral DL approaches (91) are yet to be deployed in animal ecology, where they could help advancing the characterization of habitats.”