LSTM-DNN Based Autoencoder Network for Nonlinear Hyperspectral Image Unmixing

Blind hyperspectral unmixing is an important technique in hyperspectral image analysis, aiming at estimating endmembers and their respective fractional abundances. Consider the limitations of using the linear model, nonlinear unmixing methods have been studied under different model assumptions. Howe...

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Vydané v:	IEEE journal of selected topics in signal processing Ročník 15; číslo 2; s. 295 - 309
Hlavní autori:	Zhao, Min, Yan, Longbin, Chen, Jie
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.02.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithms Artificial neural networks attention recurrent neural network autoencoder network Correlation Decoding Hyperspectral imaging Hyperspectral unmixing Image analysis Mathematical model Neural networks nonlinear unmixing Regularization Spectral correlation Task analysis Three-dimensional displays
ISSN:	1932-4553, 1941-0484
On-line prístup:	Získať plný text
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