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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Bibliographic Details
Published in:	IEEE journal of selected topics in signal processing Vol. 15; no. 2; pp. 295 - 309
Main Authors:	Zhao, Min, Yan, Longbin, Chen, Jie
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.02.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	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
Online Access:	Get full text
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