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Avoiding Overfitting When Applying Spectral-Spatial Deep Learning Methods on Hyperspectral Images with Limited Labels | IEEE Conference Publication | IEEE Xplore

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

Publisher: IEEE

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 trainin...View more

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.
Date of Conference: 28 July 2019 - 02 August 2019
Date Added to IEEE Xplore: 14 November 2019
ISBN Information:

ISSN Information:

Publisher: IEEE
Conference Location: Yokohama, Japan

References

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