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Article

Spatial Low-Rank Tensor Factorization and Unmixing of Hyperspectral Images

Department of Computer Science and Engineering, University of Puerto Rico, 00681 Mayaguez, Puerto Rico
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Author to whom correspondence should be addressed.
Academic Editor: Lucia Maddalena
Received: 26 April 2021 / Revised: 22 May 2021 / Accepted: 24 May 2021 / Published: 11 June 2021
This work presents a method for hyperspectral image unmixing based on non-negative tensor factorization. While traditional approaches may process spectral information without regard for spatial structures in the dataset, tensor factorization preserves the spectral-spatial relationship which we intend to exploit. We used a rank-(L, L, 1) decomposition, which approximates the original tensor as a sum of R components. Each component is a tensor resulting from the multiplication of a low-rank spatial representation and a spectral vector. Our approach uses spatial factors to identify high abundance areas where pure pixels (endmembers) may lie. Unmixing is done by applying Fully Constrained Least Squares such that abundance maps are produced for each inferred endmember. The results of this method are compared against other approaches based on non-negative matrix and tensor factorization. We observed a significant reduction of spectral angle distance for extracted endmembers and equal or better RMSE for abundance maps as compared with existing benchmarks. View Full-Text
Keywords: spatial low-rank tensor decomposition; remote sensing; hyperspectral image unmixing spatial low-rank tensor decomposition; remote sensing; hyperspectral image unmixing
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MDPI and ACS Style

Navas-Auger, W.; Manian, V. Spatial Low-Rank Tensor Factorization and Unmixing of Hyperspectral Images. Computers 2021, 10, 78. https://0-doi-org.brum.beds.ac.uk/10.3390/computers10060078

AMA Style

Navas-Auger W, Manian V. Spatial Low-Rank Tensor Factorization and Unmixing of Hyperspectral Images. Computers. 2021; 10(6):78. https://0-doi-org.brum.beds.ac.uk/10.3390/computers10060078

Chicago/Turabian Style

Navas-Auger, William, and Vidya Manian. 2021. "Spatial Low-Rank Tensor Factorization and Unmixing of Hyperspectral Images" Computers 10, no. 6: 78. https://0-doi-org.brum.beds.ac.uk/10.3390/computers10060078

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