CyCU-Net: Cycle-Consistency Unmixing Network by Learning Cascaded Autoencoders
In recent years, deep learning (DL) has attracted increasing attention in hyperspectral unmixing (HU) applications due to its powerful learning and data fitting ability. The autoencoder (AE) framework, as an unmixing baseline network, achieves good performance in HU by automatically learning low-dim...
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| Vydáno v: | IEEE transactions on geoscience and remote sensing Ročník 60; s. 1 - 14 |
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| Hlavní autoři: | , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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New York
IEEE
01.01.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Institute of Electrical and Electronics Engineers |
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| ISSN: | 0196-2892, 1558-0644 |
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| Abstract | In recent years, deep learning (DL) has attracted increasing attention in hyperspectral unmixing (HU) applications due to its powerful learning and data fitting ability. The autoencoder (AE) framework, as an unmixing baseline network, achieves good performance in HU by automatically learning low-dimensional embeddings and reconstructing data. Nevertheless, the conventional AE-based architecture, which focuses more on the pixel-level reconstruction loss, tends to lose some significant detailed information of certain materials (e.g., material-related properties) in the reconstruction process. Therefore, inspired by the perception mechanism, we propose a cycle-consistency unmixing network, called CyCU-Net, by learning two cascaded AEs in an end-to-end fashion, to enhance the unmixing performance more effectively. CyCU-Net is capable of reducing the detailed and material-related information loss in the process of reconstruction by relaxing the original pixel-level reconstruction assumption to cycle consistency dominated by the cascaded AEs. More specifically, cycle consistency can be achieved by a newly proposed self-perception loss, which consists of two spectral reconstruction terms and one abundance reconstruction term. By taking advantage of the self-perception loss in the network, the high-level semantic information can be well preserved in the unmixing process. Moreover, we investigate the performance gain of CyCU-Net with extensive ablation studies. Experimental results on one synthetic and three real hyperspectral data sets demonstrate the effectiveness and competitiveness of the proposed CyCU-Net in comparison with several state-of-the-art unmixing algorithms. |
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| AbstractList | In recent years, deep learning (DL) has attracted increasing attention in hyperspectral unmixing (HU) applications due to its powerful learning and data fitting ability. The autoencoder (AE) framework, as an unmixing baseline network, achieves good performance in HU by automatically learning low-dimensional embeddings and reconstructing data. Nevertheless, the conventional AE-based architecture, which focuses more on the pixel-level reconstruction loss, tends to lose some significant detailed information of certain materials (e.g., material-related properties) in the reconstruction process. Therefore, inspired by the perception mechanism, we propose a cycle-consistency unmixing network, called CyCU-Net, by learning two cascaded AEs in an end-to-end fashion, to enhance the unmixing performance more effectively. CyCU-Net is capable of reducing the detailed and material-related information loss in the process of reconstruction by relaxing the original pixel-level reconstruction assumption to cycle consistency dominated by the cascaded AEs. More specifically, cycle consistency can be achieved by a newly proposed self-perception loss, which consists of two spectral reconstruction terms and one abundance reconstruction term. By taking advantage of the self-perception loss in the network, the high-level semantic information can be well preserved in the unmixing process. Moreover, we investigate the performance gain of CyCU-Net with extensive ablation studies. Experimental results on one synthetic and three real hyperspectral data sets demonstrate the effectiveness and competitiveness of the proposed CyCU-Net in comparison with several state-of-the-art unmixing algorithms. |
| Author | Gao, Lianru Chanussot, Jocelyn Hong, Danfeng Han, Zhu Zhang, Bing |
| Author_xml | – sequence: 1 givenname: Lianru orcidid: 0000-0003-3888-8124 surname: Gao fullname: Gao, Lianru email: gaolr@aircas.ac.cn organization: Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China – sequence: 2 givenname: Zhu orcidid: 0000-0002-8602-864X surname: Han fullname: Han, Zhu email: hanzhu19@mails.ucas.ac.cn organization: Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China – sequence: 3 givenname: Danfeng orcidid: 0000-0002-3212-9584 surname: Hong fullname: Hong, Danfeng email: danfeng.hong@dlr.de organization: Remote Sensing Technology Institute (IMF), German Aerospace Center (DLR), Wessling, Germany – sequence: 4 givenname: Bing orcidid: 0000-0001-7311-9844 surname: Zhang fullname: Zhang, Bing email: zb@radi.ac.cn organization: Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China – sequence: 5 givenname: Jocelyn orcidid: 0000-0003-4817-2875 surname: Chanussot fullname: Chanussot, Jocelyn email: jocelyn@hi.is organization: Université. Grenoble Alpes, INRIA, CNRS, Grenoble INP, LJK, Grenoble, France |
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| SubjectTerms | Ablation Algorithms Atmospheric modeling Cascaded autoencoders (AEs) Competitiveness Computer Science Consistency cycle consistency Decoding Deep learning deep learning (DL) Feature extraction Hyperspectral imaging hyperspectral unmixing (HU) Image reconstruction Information processing Machine learning Perception Pixels Reconstruction remote sensing (RS) Self image self-perception Semantics Signal and Image Processing |
| Title | CyCU-Net: Cycle-Consistency Unmixing Network by Learning Cascaded Autoencoders |
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