Hyperspectral Unmixing for Additive Nonlinear Models With a 3-D-CNN Autoencoder Network
Spectral unmixing is an important task in hyperspectral image processing for separating the mixed spectral data pertaining to various materials observed aiming at analyzing the material components in observed pixels. Recently, nonlinear spectral unmixing has received particular attention in hyperspe...
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| Vydané v: | IEEE transactions on geoscience and remote sensing Ročník 60; s. 1 - 15 |
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| Hlavní autori: | , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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New York
IEEE
2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 0196-2892, 1558-0644 |
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| Abstract | Spectral unmixing is an important task in hyperspectral image processing for separating the mixed spectral data pertaining to various materials observed aiming at analyzing the material components in observed pixels. Recently, nonlinear spectral unmixing has received particular attention in hyperspectral image processing, as there are many situations in which the linear mixture model may not be appropriate and could be advantageously replaced by a nonlinear one. Existing nonlinear unmixing approaches are often based on specific assumptions on the nonlinearity and can be less effective when used for scenes with unknown nonlinearity. This article presents an unsupervised nonlinear spectral unmixing method that addresses a general model that consists of a linear mixture part and an additive nonlinear mixture part. The structure of a deep autoencoder network, which has a clear physical interpretation, is specifically designed to achieve this purpose. Moreover, a convolutional neural network (CNN) is used to capture the spectral-spatial priors from hyperspectral data. Extensive experiments with synthetic and real data illustrate the generality and effectiveness of this scheme compared with state-of-the-art methods. |
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| AbstractList | Spectral unmixing is an important task in hyperspectral image processing for separating the mixed spectral data pertaining to various materials observed aiming at analyzing the material components in observed pixels. Recently, nonlinear spectral unmixing has received particular attention in hyperspectral image processing, as there are many situations in which the linear mixture model may not be appropriate and could be advantageously replaced by a nonlinear one. Existing nonlinear unmixing approaches are often based on specific assumptions on the nonlinearity and can be less effective when used for scenes with unknown nonlinearity. This article presents an unsupervised nonlinear spectral unmixing method that addresses a general model that consists of a linear mixture part and an additive nonlinear mixture part. The structure of a deep autoencoder network, which has a clear physical interpretation, is specifically designed to achieve this purpose. Moreover, a convolutional neural network (CNN) is used to capture the spectral-spatial priors from hyperspectral data. Extensive experiments with synthetic and real data illustrate the generality and effectiveness of this scheme compared with state-of-the-art methods. |
| Author | Rahardja, Susanto Zhao, Min Wang, Mou Chen, Jie |
| Author_xml | – sequence: 1 givenname: Min surname: Zhao fullname: Zhao, Min email: minzhao@mail.nwpu.edu.cn organization: School of Marine Science and Technology, Northwestern Polytechnical University, Xi'an, China – sequence: 2 givenname: Mou orcidid: 0000-0002-6476-2501 surname: Wang fullname: Wang, Mou email: mangmou21@mail.nwpu.edu.cn organization: School of Marine Science and Technology, Northwestern Polytechnical University, Xi'an, China – sequence: 3 givenname: Jie orcidid: 0000-0003-2306-8860 surname: Chen fullname: Chen, Jie email: dr.jie.chen@ieee.org organization: School of Marine Science and Technology, Northwestern Polytechnical University, Xi'an, China – sequence: 4 givenname: Susanto orcidid: 0000-0003-0831-6934 surname: Rahardja fullname: Rahardja, Susanto email: susantorahardja@ieee.org organization: School of Marine Science and Technology, Northwestern Polytechnical University, Xi'an, China |
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| SubjectTerms | 3-D-convolutional neural network (CNN) Additives Artificial neural networks autoencoder network Decoding Hyperspectral imaging Image processing Kernel Mixture models Neural networks nonlinear spectral unmixing Nonlinear systems Nonlinearity Photonics Spectra |
| Title | Hyperspectral Unmixing for Additive Nonlinear Models With a 3-D-CNN Autoencoder Network |
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