Pansharpening is usually related to the fusion of a high spatial resolution but low spectral resolution (panchromatic) image with a high spectral resolution but low spatial resolution (multispectral) image. The calculation of injection coefficients through regression is a very popular and powerful approach. These coefficients are usually estimated at reduced resolution. In this paper, the estimation of the injection coefficients at full resolution for regression-based pansharpening approaches is proposed. To this aim, an iterative algorithm is proposed and studied. Its convergence, whatever the initial guess, is demonstrated in all the practical cases and the reached asymptotic value is analytically calculated. The performance is assessed both at reduced resolution and at full resolution on four data sets acquired by the IKONOS sensor and the WorldView-3 sensor. The proposed full scale approach always shows the best performance with respect to the benchmark consisting of state-of-the-art pansharpening methods.

Full Scale Regression-Based Injection Coefficients for Panchromatic Sharpening

Gemine Vivone
;
R. Restaino;
2018-01-01

Abstract

Pansharpening is usually related to the fusion of a high spatial resolution but low spectral resolution (panchromatic) image with a high spectral resolution but low spatial resolution (multispectral) image. The calculation of injection coefficients through regression is a very popular and powerful approach. These coefficients are usually estimated at reduced resolution. In this paper, the estimation of the injection coefficients at full resolution for regression-based pansharpening approaches is proposed. To this aim, an iterative algorithm is proposed and studied. Its convergence, whatever the initial guess, is demonstrated in all the practical cases and the reached asymptotic value is analytically calculated. The performance is assessed both at reduced resolution and at full resolution on four data sets acquired by the IKONOS sensor and the WorldView-3 sensor. The proposed full scale approach always shows the best performance with respect to the benchmark consisting of state-of-the-art pansharpening methods.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11386/4716457
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