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Regularised IHS-based pan-sharpening approach using spectral consistency constraint and total variation

Regularised IHS-based pan-sharpening approach using spectral consistency constraint and total variation

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In this study, the authors address the fusion of low-resolution multi-spectral image with the corresponding high-resolution panchromatic image to provide high-resolution multi-spectral (HRM) one, i.e. pan sharpening. The intensity–hue–saturation (IHS)-based pan-sharpening methods are popular because they are simple, efficient, and of high-spatial quality. However, their frameworks are unavoidably subject to spectral distortion. To reduce the inevitable spectral distortion of IHS-based pan-sharpening approaches, the spectral consistency constraint is used in the proposed method. Moreover, to stabilise fusion results obtained from the ill-posed pan-sharpening problem and to keep the smoothness of the HRM image, a total variation regularisation term is considered. These considerations are formulated in a non-quadratic optimisation problem. To solve this problem, a kind of variable splitting method, known as half-quadratic approximation is utilised, and also an alternating optimisation procedure is used to reconstruct HRM image. To gain convenient control on the local spectral and the spatial information, and also to reduce the required memory, in the optimisation stage, the patch-based strategy is employed. The proposed method was tested on two datasets acquired by GeoEye-1 and Pleiades satellites. To evaluate the proposed method, visual assessment, as well as quantitative comparison with different pan-sharpening methods, was carried out.

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