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

Nezha Farhi; Moussa Sofiane Karoui; Khelifa Djerriri; Issam Boukerch

doi:10.1117/12.2241408

18 October 2016 Pansharpening remotely sensed data by using nonnegative matrix factorization and spectral-spatial degradation models

Nezha Farhi, Moussa Sofiane Karoui, Khelifa Djerriri, Issam Boukerch

Author Affiliations +

Proceedings Volume 10004, Image and Signal Processing for Remote Sensing XXII; 1000407 (2016) https://doi.org/10.1117/12.2241408
Event: SPIE Remote Sensing, 2016, Edinburgh, United Kingdom

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.

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Nezha Farhi, Nezha Farhi, Moussa Sofiane Karoui, Moussa Sofiane Karoui, Khelifa Djerriri, Khelifa Djerriri, Issam Boukerch, Issam Boukerch, "Pansharpening remotely sensed data by using nonnegative matrix factorization and spectral-spatial degradation models", Proc. SPIE 10004, Image and Signal Processing for Remote Sensing XXII, 1000407 (18 October 2016); doi: 10.1117/12.2241408; https://doi.org/10.1117/12.2241408

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