SSF-Net: A Spatial-Spectral Features Integrated Autoencoder Network for Hyperspectral Unmixing

In recent years, deep learning (DL) has received tremendous attention in the field of hyperspectral unmixing (HU) due to its powerful learning capabilities. Particularly, the unsupervised unmixing method based on autoencoder (AE) has become a research hotspot. Most of the current AE unmixing network...

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Vydané v:	IEEE journal of selected topics in applied earth observations and remote sensing Ročník 17; s. 1 - 14
Hlavní autori:	Wang, Bin, Yao, Huizheng, Song, Dongmei, Zhang, Jie, Gao, Han
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 01.01.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithms Attention autoencoder (AE) Data models Decoding Deep learning deep learning (DL) Electromagnetic scattering Feature extraction feature fusion Heterogeneity Hyperspectral imaging hyperspectral unmixing (HU) Information processing Machine learning Patchiness Spatial heterogeneity Task analysis
ISSN:	1939-1404, 2151-1535
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Abstract	In recent years, deep learning (DL) has received tremendous attention in the field of hyperspectral unmixing (HU) due to its powerful learning capabilities. Particularly, the unsupervised unmixing method based on autoencoder (AE) has become a research hotspot. Most of the current AE unmixing networks mainly focus on information about pixels and their neighborhoods in images. However, they make insufficient use of information about spatial heterogeneity and spectral differences of endmembers in HSI data. To this end, an AE hyperspectral un-mixing network with the name of SSF-Net is proposed for fusing the spatial-spectral features. The network first extracts pseudo-endmember information from the HSI using a regional VCA algorithm. Then, a dual-branch feature fusion module incorporating a spatial-spectral attention mechanism is constructed to make full use of the information in the HSI data, thereby im-proving the network's unmixing performance. It is worth stating that SSF-Net can fuse spatial spectral information and utilize different attention maps to obtain more significant spectral difference information and more discriminative spatial difference information about the scene. Experimental results on synthetic and real datasets demonstrate that the proposed SSF-Net out-performs state-of-the-art unmixing algorithms.
AbstractList	In recent years, deep learning has received tremendous attention in the field of hyperspectral unmixing (HU) due to its powerful learning capabilities. Particularly, the unsupervised unmixing method based on an autoencoder (AE) has become a research hotspot. Most of the current AE unmixing networks mainly focus on information about pixels and their neighborhoods in images. However, they make insufficient use of information about spatial heterogeneity and spectral differences of endmembers in hyperspectral image (HSI) data. To this end, an AE HU network with the name of SSF-Net is proposed for fusing the spatial–spectral features. The network first extracts pseudoendmember information from the HSI using a regional vertex component analysis algorithm. Then, a dual-branch feature fusion module incorporating a spatial–spectral attention mechanism is constructed to make full use of the information in the HSI data, thereby improving the network's unmixing performance. It is worth stating that SSF-Net can fuse spatial–spectral information and utilize different attention maps to obtain more significant spectral difference information and more discriminative spatial difference information about the scene. The experimental results on synthetic and real datasets demonstrate that the proposed SSF-Net outperforms state-of-the-art unmixing algorithms. In recent years, deep learning (DL) has received tremendous attention in the field of hyperspectral unmixing (HU) due to its powerful learning capabilities. Particularly, the unsupervised unmixing method based on autoencoder (AE) has become a research hotspot. Most of the current AE unmixing networks mainly focus on information about pixels and their neighborhoods in images. However, they make insufficient use of information about spatial heterogeneity and spectral differences of endmembers in HSI data. To this end, an AE hyperspectral un-mixing network with the name of SSF-Net is proposed for fusing the spatial-spectral features. The network first extracts pseudo-endmember information from the HSI using a regional VCA algorithm. Then, a dual-branch feature fusion module incorporating a spatial-spectral attention mechanism is constructed to make full use of the information in the HSI data, thereby im-proving the network's unmixing performance. It is worth stating that SSF-Net can fuse spatial spectral information and utilize different attention maps to obtain more significant spectral difference information and more discriminative spatial difference information about the scene. Experimental results on synthetic and real datasets demonstrate that the proposed SSF-Net out-performs state-of-the-art unmixing algorithms.
Author	Song, Dongmei Yao, Huizheng Gao, Han Zhang, Jie Wang, Bin
Author_xml	– sequence: 1 givenname: Bin surname: Wang fullname: Wang, Bin organization: College of Oceanography and Space Informatics, China University of Petrole -um (East China), Qingdao, China – sequence: 2 givenname: Huizheng surname: Yao fullname: Yao, Huizheng organization: College of Oceanography and Space Informatics, China University of Petrole -um (East China), Qingdao, China – sequence: 3 givenname: Dongmei surname: Song fullname: Song, Dongmei organization: College of Oceanography and Space Informatics, China University of Petrole -um (East China), Qingdao, China – sequence: 4 givenname: Jie surname: Zhang fullname: Zhang, Jie organization: College of Oceanography and Space Informatics, China University of Petrole -um (East China), Qingdao, China – sequence: 5 givenname: Han surname: Gao fullname: Gao, Han organization: College of Oceanography and Space Informatics, China University of Petrole -um (East China), Qingdao, China
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Snippet	In recent years, deep learning (DL) has received tremendous attention in the field of hyperspectral unmixing (HU) due to its powerful learning capabilities.... In recent years, deep learning has received tremendous attention in the field of hyperspectral unmixing (HU) due to its powerful learning capabilities....
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SubjectTerms	Algorithms Attention autoencoder (AE) Data models Decoding Deep learning deep learning (DL) Electromagnetic scattering Feature extraction feature fusion Heterogeneity Hyperspectral imaging hyperspectral unmixing (HU) Information processing Machine learning Patchiness Spatial heterogeneity Task analysis
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Title	SSF-Net: A Spatial-Spectral Features Integrated Autoencoder Network for Hyperspectral Unmixing
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