Deep neural networks-based relevant latent representation learning for hyperspectral image classification

A Sellami, S Tabbone - Pattern Recognition, 2022 - Elsevier
The classification of hyperspectral image is a challenging task due to the high dimensional
space, with large number of spectral bands, and low number of labeled training samples. To …
被引用次数：135 相关文章

Deep neural networks-based relevant latent representation learning for hyperspectral image classification

A Sellami, S Tabbone - Pattern Recognition, 2022 - ui.adsabs.harvard.edu
The classification of hyperspectral image is a challenging task due to the high dimensional
space, with large number of spectral bands, and low number of labeled training samples. To …

Deep neural networks-based relevant latent representation learning for hyperspectral image classification

A Sellami, S Tabbone - 2022 - dl.acm.org
The classification of hyperspectral image is a challenging task due to the high dimensional
space, with large number of spectral bands, and low number of labeled training samples. To …

Deep neural networks-based relevant latent representation learning for hyperspectral image classification

A Sellami, S Tabbone - Pattern Recognition, 2022 - hal.science
The classification of hyperspectral image is a challenging task due to the high dimensional
space, with large number of spectral bands, and low number of labeled training samples. To …
[PDF] hal.science

[PDF][PDF] Deep neural networks-based relevant latent representation learning for hyperspectral image classification

A Sellami, S Tabbone - 2021 - hal.science
The classification of hyperspectral image is a challenging task due to the high dimensional
space, with large number of spectral bands, and low number of labeled training samples. To …