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Proceedings Paper

Pansharpening remotely sensed data by using nonnegative matrix factorization and spectral-spatial degradation models
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Paper Abstract

In this paper, a new pansharpening method, which uses nonnegative matrix factorization, is proposed to enhance the spatial resolution of remote sensing multispectral images. This method, based on the linear spectral unmixing concept and called joint spatial-spectral variables nonnegative matrix factorization, optimizes, by new iterative and multiplicative update rules, a joint-variables criterion that exploits spatial and spectral degradation models between the considered images. This criterion considers only two unknown high spatial-spectral resolutions variables. The proposed method is tested on synthetic and real datasets and its effectiveness, in spatial and spectral domains, is evaluated with established performance criteria. Results show the good performances of the proposed approach in comparison with other standard literature ones.

Paper Details

Date Published: 18 October 2016
PDF: 10 pages
Proc. SPIE 10004, Image and Signal Processing for Remote Sensing XXII, 1000407 (18 October 2016); doi: 10.1117/12.2241408
Show Author Affiliations
Nezha Farhi, Ctr. National des Techniques Spatiales (Algeria)
Moussa Sofiane Karoui, Ctr. National des Techniques Spatiales (Algeria)
Khelifa Djerriri, Ctr. National des Techniques Spatiales (Algeria)
Issam Boukerch, Ctr. National des Techniques Spatiales (Algeria)

Published in SPIE Proceedings Vol. 10004:
Image and Signal Processing for Remote Sensing XXII
Lorenzo Bruzzone; Francesca Bovolo, Editor(s)

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