Spectral-Spatial Feature Extraction With Dual Graph Autoencoder for Hyperspectral Image Clustering

Autoencoder (AE) is an unsupervised neural network framework for efficient and effective feature extraction. Most AE-based methods do not consider spatial information and band correlations for hyperspectral image (HSI) analysis. In addition, graph-based AE methods often learn discriminative represen...

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Vydané v:	IEEE transactions on circuits and systems for video technology Ročník 32; číslo 12; s. 8500 - 8511
Hlavní autori:	Zhang, Yongshan, Wang, Yang, Chen, Xiaohong, Jiang, Xinwei, Zhou, Yicong
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.12.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	autoencoder Band spectra Banded structure Clustering Coders Convolution Data mining Decoding dimensionality reduction Encoders-Decoders Feature extraction graph convolution Graph neural networks Graphical representations Hyperspectral imagery Hyperspectral imaging Pixels Principal component analysis Similarity Source code Spatial data Spectral bands Task analysis
ISSN:	1051-8215, 1558-2205
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Abstract	Autoencoder (AE) is an unsupervised neural network framework for efficient and effective feature extraction. Most AE-based methods do not consider spatial information and band correlations for hyperspectral image (HSI) analysis. In addition, graph-based AE methods often learn discriminative representations with the assumption that connected samples share the same label and they cannot directly embed the geometric structure into feature extraction. To address these issues, in this paper, we propose a dual graph autoencoder (DGAE) to learn discriminative representations for HSIs. Utilizing the relationships of pair-wise pixels within homogenous regions and pair-wise spectral bands, DGAE first constructs the superpixel-based similarity graph with spatial information and band-based similarity graph to characterize the geometric structures of HSIs. With the developed dual graph convolution, more discriminative feature representations are learnt from the hidden layer via the encoder-decoder structure of DGAE. The main advantage of DGAE is that it fully exploits both the geometric structures of pixels with spatial information and spectral bands to promote nonlinear feature extraction of HSIs. Experiments on HSI datasets show the superiority of the proposed DGAE over the state-of-the-art methods. The source code of DGAE is available at https://github.com/ZhangYongshan/DGAE .
AbstractList	Autoencoder (AE) is an unsupervised neural network framework for efficient and effective feature extraction. Most AE-based methods do not consider spatial information and band correlations for hyperspectral image (HSI) analysis. In addition, graph-based AE methods often learn discriminative representations with the assumption that connected samples share the same label and they cannot directly embed the geometric structure into feature extraction. To address these issues, in this paper, we propose a dual graph autoencoder (DGAE) to learn discriminative representations for HSIs. Utilizing the relationships of pair-wise pixels within homogenous regions and pair-wise spectral bands, DGAE first constructs the superpixel-based similarity graph with spatial information and band-based similarity graph to characterize the geometric structures of HSIs. With the developed dual graph convolution, more discriminative feature representations are learnt from the hidden layer via the encoder-decoder structure of DGAE. The main advantage of DGAE is that it fully exploits both the geometric structures of pixels with spatial information and spectral bands to promote nonlinear feature extraction of HSIs. Experiments on HSI datasets show the superiority of the proposed DGAE over the state-of-the-art methods. The source code of DGAE is available at https://github.com/ZhangYongshan/DGAE .
Author	Wang, Yang Jiang, Xinwei Zhou, Yicong Zhang, Yongshan Chen, Xiaohong
Author_xml	– sequence: 1 givenname: Yongshan orcidid: 0000-0001-5817-1732 surname: Zhang fullname: Zhang, Yongshan email: yszhang.cug@gmail.com organization: School of Computer Science and the Hubei Key Laboratory of Intelligent Geo-Information Processing, China University of Geosciences, Wuhan, China – sequence: 2 givenname: Yang surname: Wang fullname: Wang, Yang organization: School of Computer Science and the Hubei Key Laboratory of Intelligent Geo-Information Processing, China University of Geosciences, Wuhan, China – sequence: 3 givenname: Xiaohong surname: Chen fullname: Chen, Xiaohong organization: School of Computer Science and the Hubei Key Laboratory of Intelligent Geo-Information Processing, China University of Geosciences, Wuhan, China – sequence: 4 givenname: Xinwei orcidid: 0000-0001-6783-2176 surname: Jiang fullname: Jiang, Xinwei email: ysjxw@hotmail.com organization: School of Computer Science and the Hubei Key Laboratory of Intelligent Geo-Information Processing, China University of Geosciences, Wuhan, China – sequence: 5 givenname: Yicong orcidid: 0000-0002-4487-6384 surname: Zhou fullname: Zhou, Yicong email: yicongzhou@um.edu.mo organization: Department of Computer and Information Science, University of Macau, Taipa, Macau, China
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SubjectTerms	autoencoder Band spectra Banded structure Clustering Coders Convolution Data mining Decoding dimensionality reduction Encoders-Decoders Feature extraction graph convolution Graph neural networks Graphical representations Hyperspectral imagery Hyperspectral imaging Pixels Principal component analysis Similarity Source code Spatial data Spectral bands Task analysis
Title	Spectral-Spatial Feature Extraction With Dual Graph Autoencoder for Hyperspectral Image Clustering
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