Multi-Scale Fusion Maximum Entropy Subspace Clustering for Hyperspectral Band Selection
A novel multi-scale fusion maximum entropy subspace clustering (MFMESC) for hyperspectral image (HSI) band selection is proposed in this paper. Subspace clustering is combined as a self-expression layer with stacked convolutional autoencoder, so that subspace clustering working in linear subspaces c...
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| Vydané v: | IEEE International Geoscience and Remote Sensing Symposium proceedings s. 779 - 782 |
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| Hlavní autori: | , , , , , |
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| Jazyk: | English |
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IEEE
17.07.2022
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| ISSN: | 2153-7003 |
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| Abstract | A novel multi-scale fusion maximum entropy subspace clustering (MFMESC) for hyperspectral image (HSI) band selection is proposed in this paper. Subspace clustering is combined as a self-expression layer with stacked convolutional autoencoder, so that subspace clustering working in linear subspaces can deal with complicated HSI data with nonlinear characteristics. Multiple fully-connected linear layers are inserted between the encoder layers and their corresponding decoder layers to promote learning more favorable representations for subspace clustering. A multi-scale fusion module is designed to guide the fusion of multi-scale information extracted from different layers to learn a more discriminative self-expression coefficient matrix. Furthermore, the maximum entropy regularization is introduced in the subspace clustering to promote the connectivity within each subspace. Experimental results demonstrate the superiority of the proposed model against state of-the-art methods. |
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| AbstractList | A novel multi-scale fusion maximum entropy subspace clustering (MFMESC) for hyperspectral image (HSI) band selection is proposed in this paper. Subspace clustering is combined as a self-expression layer with stacked convolutional autoencoder, so that subspace clustering working in linear subspaces can deal with complicated HSI data with nonlinear characteristics. Multiple fully-connected linear layers are inserted between the encoder layers and their corresponding decoder layers to promote learning more favorable representations for subspace clustering. A multi-scale fusion module is designed to guide the fusion of multi-scale information extracted from different layers to learn a more discriminative self-expression coefficient matrix. Furthermore, the maximum entropy regularization is introduced in the subspace clustering to promote the connectivity within each subspace. Experimental results demonstrate the superiority of the proposed model against state of-the-art methods. |
| Author | Zhao, Enyu Yu, Chunyan Wang, Yulei Song, Meiping Jiang, Liru Ma, Haipeng |
| Author_xml | – sequence: 1 givenname: Haipeng surname: Ma fullname: Ma, Haipeng organization: Center for Hyperspectral Imaging in Remote Sensing (CHIRS), Information Science and Technology College, Dalian Maritime University,Dalian,China,116026 – sequence: 2 givenname: Yulei surname: Wang fullname: Wang, Yulei organization: Center for Hyperspectral Imaging in Remote Sensing (CHIRS), Information Science and Technology College, Dalian Maritime University,Dalian,China,116026 – sequence: 3 givenname: Liru surname: Jiang fullname: Jiang, Liru organization: Center for Hyperspectral Imaging in Remote Sensing (CHIRS), Information Science and Technology College, Dalian Maritime University,Dalian,China,116026 – sequence: 4 givenname: Meiping surname: Song fullname: Song, Meiping organization: Center for Hyperspectral Imaging in Remote Sensing (CHIRS), Information Science and Technology College, Dalian Maritime University,Dalian,China,116026 – sequence: 5 givenname: Chunyan surname: Yu fullname: Yu, Chunyan organization: Center for Hyperspectral Imaging in Remote Sensing (CHIRS), Information Science and Technology College, Dalian Maritime University,Dalian,China,116026 – sequence: 6 givenname: Enyu surname: Zhao fullname: Zhao, Enyu organization: Center for Hyperspectral Imaging in Remote Sensing (CHIRS), Information Science and Technology College, Dalian Maritime University,Dalian,China,116026 |
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| Snippet | A novel multi-scale fusion maximum entropy subspace clustering (MFMESC) for hyperspectral image (HSI) band selection is proposed in this paper. Subspace... |
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| SubjectTerms | Benchmark testing Data mining Decoding Entropy Geoscience and remote sensing hyperspectral band selection Hyperspectral imaging maximum entropy regularization multi-scale fusion stacked convolutional autoencoder subspace clustering |
| Title | Multi-Scale Fusion Maximum Entropy Subspace Clustering for Hyperspectral Band Selection |
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