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A Multi-Attention Autoencoder for Hyperspectral Unmixing Based on the Extended Linear Mixing Model

Hyperspectral unmixing, which decomposes mixed pixels into the endmembers and corresponding abundances, is an important image process for the further application of hyperspectral images (HSIs). Lately, the unmixing problem has been solved using deep learning techniques, particularly autoencoders (AE...

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Principais autores: Lijuan Su, Jun Liu, Yan Yuan, Qiyue Chen
פורמט: Artigo
שפה:Inglês
יצא לאור: MDPI AG 2023-06-01
סדרה:Remote Sensing
נושאים:
hyperspectral unmixing
autoencoder
spatial–spectral attention
spectral variability
spatial homogeneity
גישה מקוונת:https://www.mdpi.com/2072-4292/15/11/2898
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