Avoiding Overfitting When Applying Spectral-Spatial Deep Learning Methods on Hyperspectral Images with Limited Labels

Matthieu Molinier (Corresponding author), Jorma Kilpi

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

Abstract

Spatial-spectral approaches applied on hyperspectral images (HSI) with limited labels suffer from overfitting when the size of input filters and the percentage of training data increases. In those cases, pixel values corresponding to testing sets are partly or completely seen during training phase, reducing the number independent testing pixels and leading to overoptimistic accuracy assessment. These effects have been demonstrated in several previous works but still require attention. In this work we propose additional visulizations and measures of the overlapping and overfitting effects, demonstrated on common HSI datasets, to increase awareness on these issues.
Original languageEnglish
Title of host publicationProceedings of IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium
PublisherIEEE Institute of Electrical and Electronic Engineers
Pages5049-5052
Number of pages4
ISBN (Electronic)978-1-5386-9154-0
ISBN (Print)978-1-5386-9155-7
DOIs
Publication statusPublished - 14 Nov 2019
MoE publication typeA4 Article in a conference publication
EventIEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Yokohama, Japan
Duration: 28 Jul 20192 Aug 2019
https://igarss2019.org

Conference

ConferenceIEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019
CountryJapan
CityYokohama
Period28/07/192/08/19
Internet address

Fingerprint

Labels
Pixels
Testing
Deep learning

Keywords

  • Deep Learning
  • Remote sensing
  • Hyperspectral imagery
  • limited training samples
  • overfitting

Cite this

Molinier, M., & Kilpi, J. (2019). Avoiding Overfitting When Applying Spectral-Spatial Deep Learning Methods on Hyperspectral Images with Limited Labels. In Proceedings of IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium (pp. 5049-5052). IEEE Institute of Electrical and Electronic Engineers . https://doi.org/10.1109/IGARSS.2019.8900328
Molinier, Matthieu ; Kilpi, Jorma. / Avoiding Overfitting When Applying Spectral-Spatial Deep Learning Methods on Hyperspectral Images with Limited Labels. Proceedings of IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium. IEEE Institute of Electrical and Electronic Engineers , 2019. pp. 5049-5052
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Molinier, M & Kilpi, J 2019, Avoiding Overfitting When Applying Spectral-Spatial Deep Learning Methods on Hyperspectral Images with Limited Labels. in Proceedings of IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium. IEEE Institute of Electrical and Electronic Engineers , pp. 5049-5052, IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019, Yokohama, Japan, 28/07/19. https://doi.org/10.1109/IGARSS.2019.8900328

Avoiding Overfitting When Applying Spectral-Spatial Deep Learning Methods on Hyperspectral Images with Limited Labels. / Molinier, Matthieu (Corresponding author); Kilpi, Jorma.

Proceedings of IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium. IEEE Institute of Electrical and Electronic Engineers , 2019. p. 5049-5052.

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

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Molinier M, Kilpi J. Avoiding Overfitting When Applying Spectral-Spatial Deep Learning Methods on Hyperspectral Images with Limited Labels. In Proceedings of IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium. IEEE Institute of Electrical and Electronic Engineers . 2019. p. 5049-5052 https://doi.org/10.1109/IGARSS.2019.8900328