10 May 2017 Hyperspectral and multispectral data fusion based on linear-quadratic nonnegative matrix factorization
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Abstract
This paper proposes three multisharpening approaches to enhance the spatial resolution of urban hyperspectral remote sensing images. These approaches, related to linear-quadratic spectral unmixing techniques, use a linear-quadratic nonnegative matrix factorization (NMF) multiplicative algorithm. These methods begin by unmixing the observable high-spectral/low-spatial resolution hyperspectral and high-spatial/low-spectral resolution multispectral images. The obtained high-spectral/high-spatial resolution features are then recombined, according to the linear-quadratic mixing model, to obtain an unobservable multisharpened high-spectral/high-spatial resolution hyperspectral image. In the first designed approach, hyperspectral and multispectral variables are independently optimized, once they have been coherently initialized. These variables are alternately updated in the second designed approach. In the third approach, the considered hyperspectral and multispectral variables are jointly updated. Experiments, using synthetic and real data, are conducted to assess the efficiency, in spatial and spectral domains, of the designed approaches and of linear NMF-based approaches from the literature. Experimental results show that the designed methods globally yield very satisfactory spectral and spatial fidelities for the multisharpened hyperspectral data. They also prove that these methods significantly outperform the used literature approaches.
© 2017 Society of Photo-Optical Instrumentation Engineers (SPIE)
Fatima Zohra Benhalouche, Fatima Zohra Benhalouche, Moussa Sofiane Karoui, Moussa Sofiane Karoui, Yannick Deville, Yannick Deville, Abdelaziz Ouamri, Abdelaziz Ouamri, } "Hyperspectral and multispectral data fusion based on linear-quadratic nonnegative matrix factorization," Journal of Applied Remote Sensing 11(2), 025008 (10 May 2017). https://doi.org/10.1117/1.JRS.11.025008 . Submission: Received: 20 November 2016; Accepted: 24 April 2017
Received: 20 November 2016; Accepted: 24 April 2017; Published: 10 May 2017
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