DAEN: Deep Autoencoder Networks for Hyperspectral Unmixing

Spectral unmixing is a technique for remotely sensed image interpretation that expresses each (possibly mixed) pixel as a combination of pure spectral signatures (endmembers) and their fractional abundances. In this paper, we develop a new technique for unsupervised unmixing which is based on a deep...

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Veröffentlicht in:	IEEE transactions on geoscience and remote sensing Jg. 57; H. 7; S. 4309 - 4321
Hauptverfasser:	Su, Yuanchao, Li, Jun, Plaza, Antonio, Marinoni, Andrea, Gamba, Paolo, Chakravortty, Somdatta
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.07.2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Computers Deep autoencoder network (DAEN) deep learning endmember identification Estimation Hyperspectral imaging hyperspectral unmixing Noise reduction Outliers (statistics) Remote sensing Signal processing Signal to noise ratio Spectra Spectral signatures Training variational autoencoder (VAE)
ISSN:	0196-2892, 1558-0644
Online-Zugang:	Volltext
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Abstract	Spectral unmixing is a technique for remotely sensed image interpretation that expresses each (possibly mixed) pixel as a combination of pure spectral signatures (endmembers) and their fractional abundances. In this paper, we develop a new technique for unsupervised unmixing which is based on a deep autoencoder network (DAEN). Our newly developed DAEN consists of two parts. The first part of the network adopts stacked autoencoders (SAEs) to learn spectral signatures, so as to generate a good initialization for the unmixing process. In the second part of the network, a variational autoencoder (VAE) is employed to perform blind source separation, aimed at obtaining the endmember signatures and abundance fractions simultaneously. By taking advantage from the SAEs, the robustness of the proposed approach is remarkable as it can unmix data sets with outliers and low signal-to-noise ratio. Moreover, the multihidden layers of the VAE ensure the required constraints (nonnegativity and sum-to-one) when estimating the abundances. The effectiveness of the proposed method is evaluated using both synthetic and real hyperspectral data. When compared with other unmixing methods, the proposed approach demonstrates very competitive performance.
AbstractList	Spectral unmixing is a technique for remotely sensed image interpretation that expresses each (possibly mixed) pixel as a combination of pure spectral signatures (endmembers) and their fractional abundances. In this paper, we develop a new technique for unsupervised unmixing which is based on a deep autoencoder network (DAEN). Our newly developed DAEN consists of two parts. The first part of the network adopts stacked autoencoders (SAEs) to learn spectral signatures, so as to generate a good initialization for the unmixing process. In the second part of the network, a variational autoencoder (VAE) is employed to perform blind source separation, aimed at obtaining the endmember signatures and abundance fractions simultaneously. By taking advantage from the SAEs, the robustness of the proposed approach is remarkable as it can unmix data sets with outliers and low signal-to-noise ratio. Moreover, the multihidden layers of the VAE ensure the required constraints (nonnegativity and sum-to-one) when estimating the abundances. The effectiveness of the proposed method is evaluated using both synthetic and real hyperspectral data. When compared with other unmixing methods, the proposed approach demonstrates very competitive performance.
Author	Li, Jun Su, Yuanchao Gamba, Paolo Marinoni, Andrea Plaza, Antonio Chakravortty, Somdatta
Author_xml	– sequence: 1 givenname: Yuanchao orcidid: 0000-0002-4776-0862 surname: Su fullname: Su, Yuanchao email: suych3@mail2.sysu.edu.cn organization: Guangdong Provincial Key Laboratory of Urbanization and Geo-Simulation, Center of Integrated Geographic Information Analysis, School of Geography and Planning, Sun Yat-sen University, Guangzhou, China – sequence: 2 givenname: Jun orcidid: 0000-0003-1613-9448 surname: Li fullname: Li, Jun email: lijun48@mail.sysu.edu.cn organization: College of Electrical and Information Engineering, Hunan University, Changsha, China – sequence: 3 givenname: Antonio orcidid: 0000-0002-9613-1659 surname: Plaza fullname: Plaza, Antonio email: aplaza@unex.es organization: Department of Technology of Computers and Communications, Hyperspectral Computing Laboratory, Escuela Politécnica, University of Extremadura, Cáceres, Spain – sequence: 4 givenname: Andrea orcidid: 0000-0001-6789-0915 surname: Marinoni fullname: Marinoni, Andrea email: andrea.marinoni@unipv.it organization: Department of Physics and Technology, Earth Observation Group, Centre for Integrated Remote Sensing and Forecasting for Arctic Operations, UiT-The Arctic University of Norway, Tromsø, Norway – sequence: 5 givenname: Paolo orcidid: 0000-0002-9576-6337 surname: Gamba fullname: Gamba, Paolo email: paolo.gamba@unipv.it organization: Department of Electrical, Computer and Biomedical Engineering, Telecommunications and Remote Sensing Laboratory, University of Pavia, Pavia, Italy – sequence: 6 givenname: Somdatta orcidid: 0000-0002-9121-4949 surname: Chakravortty fullname: Chakravortty, Somdatta email: csomdatta@rediffmail.com organization: Department of Information Technology, Maulana Abul Kalam Azad University of Technology, Kolkata, India
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Snippet	Spectral unmixing is a technique for remotely sensed image interpretation that expresses each (possibly mixed) pixel as a combination of pure spectral...
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SubjectTerms	Computers Deep autoencoder network (DAEN) deep learning endmember identification Estimation Hyperspectral imaging hyperspectral unmixing Noise reduction Outliers (statistics) Remote sensing Signal processing Signal to noise ratio Spectra Spectral signatures Training variational autoencoder (VAE)
Title	DAEN: Deep Autoencoder Networks for Hyperspectral Unmixing
URI	https://ieeexplore.ieee.org/document/8628241 https://www.proquest.com/docview/2248659287
Volume	57
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