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Spatial Low-Rank Tensor Factorization and Unmixing of Hyperspectral Images

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 inten...

Ausführliche Beschreibung

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Bibliografische Detailangaben
Hauptverfasser: William Navas-Auger, Vidya Manian
Format: Artigo
Sprache:Inglês
Veröffentlicht: MDPI AG 2021-06-01
Schriftenreihe:Computers
Schlagworte:
Online-Zugang:https://www.mdpi.com/2073-431X/10/6/78
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