Deep Learning for Classification of Hyperspectral Data: A Comparative Review
Université Paris-Saclay · Office National d'Études et de Recherches Aérospatiales
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…
Citation impact
- FWCI
- 49.11
- Percentile
- 100%
- References
- 102
Authors
3Topics & keywords
- Hyperspectral imaging
- Toolbox
- Deep learning
- Computer science
- Artificial intelligence
- Transfer of learning
- Machine learning
- Artificial neural network