Nonlinear Unmixing of Hyperspectral Data via Deep Autoencoder Networks

Nonlinear spectral unmixing is an important and challenging problem in hyperspectral image processing. Classical nonlinear algorithms are usually derived based on specific assumptions on the nonlinearity. In recent years, deep learning shows its advantage in addressing general nonlinear problems. Ho...

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Veröffentlicht in:IEEE geoscience and remote sensing letters Jg. 16; H. 9; S. 1467 - 1471
Hauptverfasser: Wang, Mou, Zhao, Min, Chen, Jie, Rahardja, Susanto
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 01.09.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Artificial neural networks
Autoencoder network
Coders
Current data
Decoding
Deep learning
Hyperspectral imaging
Image processing
Machine learning
Neural networks
nonlinear spectral unmixing
Nonlinear systems
Nonlinearity
Signal processing algorithms
Training
ISSN:1545-598X, 1558-0571
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Zusammenfassung:Nonlinear spectral unmixing is an important and challenging problem in hyperspectral image processing. Classical nonlinear algorithms are usually derived based on specific assumptions on the nonlinearity. In recent years, deep learning shows its advantage in addressing general nonlinear problems. However, existing ways of using deep neural networks for unmixing are limited and restrictive. In this letter, we develop a novel blind hyperspectral unmixing scheme based on a deep autoencoder network. Both encoder and decoder of the network are carefully designed so that we can conveniently extract estimated endmembers and abundances simultaneously from the nonlinearly mixed data. Because an autoencoder is essentially an unsupervised algorithm, this scheme only relies on the current data and, therefore, does not require additional training. Experimental results validate the proposed scheme and show its superior performance over several existing algorithms.
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ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2019.2900733