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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Vydáno v:IEEE journal of selected topics in signal processing Ročník 15; číslo 2; s. 295 - 309
Hlavní autoři: Zhao, Min, Yan, Longbin, Chen, Jie
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.02.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
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
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Shrnutí: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. However, existing nonlinear unmixing algorithms do not fully exploit spectral and spatial correlation information. This paper proposes a nonsymmetric autoencoder network to overcome this issue. The proposed scheme benefits from the universal modeling ability of deep neural networks and enables to learn the nonlinear relation from the data. Particularly, the long short-term memory network (LSTM) structure is included to capture spectral correlation information, and a spatial regularization is introduced to improve the spatial continuity of results. An attention mechanism is also used to further enhance the unmixing performance. Experiments with synthetic and real data are conducted to illustrate the effectiveness of the proposed method.
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ISSN:1932-4553
1941-0484
DOI:10.1109/JSTSP.2021.3052361