The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Deep Learning for Classification of Hyperspectral Data: A Comparative Review
Open accessAbstract
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 deep learning 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 deep learning techniques to their own data set.
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Cites
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“K 3 = This descriptor is then processed by a deep classifier, often unsupervised, such as deep belief networks in [55] and [56], restricted Boltzmann machines in [57] and [58], or cascades of autoencoders in [59]–[62].”
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Spatial-Spectral Transformer for Hyperspectral Image Classification
He, X., Chen, Y., & Lin, Z. (2021) · Remote Sensing · doi:10.3390/rs13030498 · Cited by 462
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Metadata
- Authors
- Audebert, N., Le Saux, B., & Lefevre, S.
- Venue
- IEEE Geoscience and Remote Sensing Magazine
- Published
- 2019-06
- Volume
- 7
- Issue
- 2
- Pages
- 159-173
- ISSN
- 2168-6831 (ISSN-L)
- 2473-2397
- 2373-7468
- OpenAlex
- W2962770389
- Cited by
- 686
- References
- 10
- FWCI
- 44.59
- Type
- journal article
- Language
- en
- Open access
- green
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