CTNet: an efficient coupled transformer network for robust hyperspectral unmixing

This study introduces the coupled transformer Network (CTNet), an architecture designed to enhance the robustness and effectiveness of hyperspectral unmixing (HSU) tasks, addressing key limitations of traditional autoencoder (AE) frameworks. Traditional AEs, consisting of an encoder and a decoder, e...

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Vydané v:	International journal of remote sensing Ročník 45; číslo 17; s. 5679 - 5712
Hlavní autori:	Meng, Fanlei, Sun, Haixin, Li, Jie, Xu, Tingfa
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	London Taylor & Francis 01.09.2024 Taylor & Francis Ltd
Predmet:	Abundance Artificial neural networks Coders Coupled transformer network cross-attention data collection Effectiveness Estimates image analysis Pixels Reconstruction robust hyperspectral unmixing Robustness (mathematics) spectral variability transcription loss Transformers uncertainty
ISSN:	0143-1161, 1366-5901, 1366-5901
On-line prístup:	Získať plný text
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