Zhou, L., Zhang, X., Wang, J. et al. (5 more authors) (2020) Subspace Structure Regularized Nonnegative Matrix Factorization for Hyperspectral Unmixing. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. ISSN 2151-1535
Abstract
Hyperspectral unmixing is a crucial task for hyperspectral images (HSI) processing, which estimates the proportions of constituent materials of a mixed pixel. Usually, the mixed pixels can be approximated using a linear mixing model. Since each material only occurs in a few pixels in real HSI, sparse nonnegative matrix factorization (NMF) and its extensions are widely used as solutions. Some recent works assume that materials are distributed in certain structures, which can be added as constraints to sparse NMF model. However, they only consider the spatial distribution within a local neighborhood and define the distribution structure manually, while ignoring the real distribution of materials that is diverse in different images. In this paper, we propose a new unmixing method that learns a subspace structure from the original image and incorporate it into the sparse NMF framework to promote unmixing performance. Based on the self-representation property of data points lying in the same subspace, the learned subspace structure can indicate the global similar graph of pixels that represents the real distribution of materials. Then the similar graph is used as a robust global spatial prior which is expected to be maintained in the decomposed abundance matrix. The experiments conducted on both simulated and real-world HSI datasets demonstrate the superior performance of our proposed method.
Metadata
Item Type: | Article |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. Further copying may not be permitted; contact the publisher for details. |
Keywords: | Hyperspectral imaging,Matrix decomposition,Sparse matrices,Graphical models,Distribution functions,Robustness,Hyperspectral unmixing,linear mixing model (LMM),nonnegative matrix factorization (NMF),subspace structure,similar graph |
Dates: |
|
Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Computer Science (York) |
Depositing User: | Pure (York) |
Date Deposited: | 24 Jul 2020 08:50 |
Last Modified: | 02 Mar 2025 00:06 |
Published Version: | https://doi.org/10.1109/JSTARS.2020.3011257 |
Status: | Published online |
Refereed: | Yes |
Identification Number: | 10.1109/JSTARS.2020.3011257 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:163748 |
Download
Filename: 09146211.pdf
Description: Subspace Structure Regularized Nonnegative Matrix Factorization for Hyperspectral Unmixing