Sparsity-Enhanced Convolutional Decomposition: A Novel Tensor-Based Paradigm for Blind Hyperspectral Unmixing

Blind hyperspectral unmixing (HU) has long been recognized as a crucial component in analyzing the hyperspectral imagery (HSI) collected by airborne and spaceborne sensors. Due to the highly ill-posed problems of such a blind source separation scheme and the effects of spectral variability in hypers...

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Published in:	IEEE transactions on geoscience and remote sensing Vol. 60; pp. 1 - 14
Main Authors:	Yao, Jing, Hong, Danfeng, Xu, Lin, Meng, Deyu, Chanussot, Jocelyn, Xu, Zongben
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.01.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Institute of Electrical and Electronics Engineers
Subjects:	Airborne sensing Algorithms Blind hyperspectral unmixing (HU) Computer Science Context modeling Convolutional codes convolutional sparse coding (CSC) Decomposition Encoding Hyperspectral imaging Ill posed problems Imagery Mathematical analysis Optimization Regularization Signal and Image Processing Signal processing Sparsity Spectra spectral bundles spectral variability (SV) Task analysis tensor decomposition Tensors Convolutional codes Tensors Encoding Task analysis Optimization Hyperspectral imaging Context modeling
ISSN:	0196-2892, 1558-0644
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Abstract	Blind hyperspectral unmixing (HU) has long been recognized as a crucial component in analyzing the hyperspectral imagery (HSI) collected by airborne and spaceborne sensors. Due to the highly ill-posed problems of such a blind source separation scheme and the effects of spectral variability in hyperspectral imaging, the ability to accurately and effectively unmixing the complex HSI still remains limited. To this end, this article presents a novel blind HU model, called sparsity-enhanced convolutional decomposition (SeCoDe), by jointly capturing spatial-spectral information of HSI in a tensor-based fashion. SeCoDe benefits from two perspectives. On the one hand, the convolutional operation is employed in SeCoDe to locally model the spatial relation between the targeted pixel and its neighbors, which can be well explained by spectral bundles that are capable of addressing spectral variabilities effectively. It maintains, on the other hand, physically continuous spectral components by decomposing the HSI along with the spectral domain. With sparsity-enhanced regularization, an alternative optimization strategy with alternating direction method of multipliers (ADMM)-based optimization algorithm is devised for efficient model inference. Extensive experiments conducted on three different data sets demonstrate the superiority of the proposed SeCoDe compared to previous state-of-the-art methods. We will also release the code at https://github.com/danfenghong/IEEE_TGRS_SeCoDe to encourage the reproduction of the given results.
AbstractList	Blind hyperspectral unmixing (HU) has long been recognized as a crucial component in analyzing the hyperspectral imagery (HSI) collected by airborne and spaceborne sensors. Due to the highly ill-posed problems of such a blind source separation scheme and the effects of spectral variability in hyperspectral imaging, the ability to accurately and effectively unmixing the complex HSI still remains limited. To this end, this article presents a novel blind HU model, called sparsity-enhanced convolutional decomposition (SeCoDe), by jointly capturing spatial–spectral information of HSI in a tensor-based fashion. SeCoDe benefits from two perspectives. On the one hand, the convolutional operation is employed in SeCoDe to locally model the spatial relation between the targeted pixel and its neighbors, which can be well explained by spectral bundles that are capable of addressing spectral variabilities effectively. It maintains, on the other hand, physically continuous spectral components by decomposing the HSI along with the spectral domain. With sparsity-enhanced regularization, an alternative optimization strategy with alternating direction method of multipliers (ADMM)-based optimization algorithm is devised for efficient model inference. Extensive experiments conducted on three different data sets demonstrate the superiority of the proposed SeCoDe compared to previous state-of-the-art methods. We will also release the code at https://github.com/danfenghong/IEEE_TGRS_SeCoDe to encourage the reproduction of the given results.
Author	Meng, Deyu Yao, Jing Chanussot, Jocelyn Hong, Danfeng Xu, Zongben Xu, Lin
Author_xml	– sequence: 1 givenname: Jing orcidid: 0000-0003-1301-9758 surname: Yao fullname: Yao, Jing email: jasonyao92@gmail.com organization: School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, China – sequence: 2 givenname: Danfeng orcidid: 0000-0002-3212-9584 surname: Hong fullname: Hong, Danfeng email: danfeng.hong@dlr.de organization: German Aerospace Center (DLR), Remote Sensing Technology Institute (IMF), Weßling, Germany – sequence: 3 givenname: Lin orcidid: 0000-0003-4373-0591 surname: Xu fullname: Xu, Lin email: lin.xu5470@gmail.com organization: Shanghai Em-Data Technology Company, Ltd., Institute of Artificial Intelligence, Shanghai, China – sequence: 4 givenname: Deyu orcidid: 0000-0002-1294-8283 surname: Meng fullname: Meng, Deyu email: dymeng@mail.xjtu.edu.cn organization: School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, China – sequence: 5 givenname: Jocelyn orcidid: 0000-0003-4817-2875 surname: Chanussot fullname: Chanussot, Jocelyn email: jocelyn@hi.is organization: INRIA, CNRS, Grenoble INP, GIPSA-lab, GIPSA-lab Grenoble Alpes, Grenoble, France – sequence: 6 givenname: Zongben surname: Xu fullname: Xu, Zongben email: zbxu@mail.xjtu.edu.cn organization: School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, China
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SubjectTerms	Airborne sensing Algorithms Blind hyperspectral unmixing (HU) Computer Science Context modeling Convolutional codes convolutional sparse coding (CSC) Decomposition Encoding Hyperspectral imaging Ill posed problems Imagery Mathematical analysis Optimization Regularization Signal and Image Processing Signal processing Sparsity Spectra spectral bundles spectral variability (SV) Task analysis tensor decomposition Tensors
Title	Sparsity-Enhanced Convolutional Decomposition: A Novel Tensor-Based Paradigm for Blind Hyperspectral Unmixing
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