End-to-End Convolutional Autoencoder for Nonlinear Hyperspectral Unmixing

Hyperspectral Unmixing is the process of decomposing a mixed pixel into its pure materials (endmembers) and estimating their corresponding proportions (abundances). Although linear unmixing models are more common due to their simplicity and flexibility, they suffer from many limitations in real worl...

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書誌詳細

主要な著者:	Mohamad Dhaini, Maxime Berar, Paul Honeine, Antonin Van Exem
フォーマット:	Artigo
言語:	Inglês
出版事項:	MDPI AG 2022-07-01
シリーズ:	Remote Sensing
主題:	convolutional neural network autoencoder hyperspectral imaging nonlinear spectral unmixing
オンライン･アクセス:	https://www.mdpi.com/2072-4292/14/14/3341
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