Deep convolutional transformer network for hyperspectral unmixing

Deep convolutional transformer network for hyperspectral unmixing

ABSTRACTHyperspectral unmixing (HU) is considered one of the most important ways to improve hyperspectral image analysis. HU aims to break down the mixed pixel into a set of spectral signatures, often commonly referred to as endmembers, and determine the fractional abundance of those endmembers. Dee...

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Bibliographic Details
Main Authors: Fazal Hadi, Jingxiang Yang, Ghulam Farooque, Liang Xiao
Format: Article
Language:English
Published: Taylor & Francis Group 2023-12-01
Series:European Journal of Remote Sensing
Subjects:
Hyperspectral unmixing
deep learning (DL)
transformer
autoencoder (AE)
tokenizer
remote sensing
Online Access:https://www.tandfonline.com/doi/10.1080/22797254.2023.2268820

Internet

https://www.tandfonline.com/doi/10.1080/22797254.2023.2268820

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