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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Vydáno v:	IEEE geoscience and remote sensing letters Ročník 16; číslo 9; s. 1467 - 1471
Hlavní autoři:	Wang, Mou, Zhao, Min, Chen, Jie, Rahardja, Susanto
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Piscataway IEEE 01.09.2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	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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Abstract	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.
AbstractList	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.
Author	Rahardja, Susanto Zhao, Min Wang, Mou Chen, Jie
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SubjectTerms	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
Title	Nonlinear Unmixing of Hyperspectral Data via Deep Autoencoder Networks
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