A novel image fusion method is presented, suitable for sharpening of multispectral (MS) images by means of a panchromatic (PAN) observation. The method is based on redundant multiresolution analysis (MRA); the MS bands expanded to the finer scale of the PAN band are sharpened by adding the spatial details from the MRA representation of the PAN data. As a direct, unconditioned injection of PAN details gives unsatisfactory results, a new injection model is proposed that provides the optimum injection by maximizing a global quality index of the fused product. To this aim, a real-valued genetic algorithm (GA) has been defined and tested on Quickbird data. The optimum GA injection is driven by an index function capable of measuring different types of possible distortions in the fused images. Fusion tests are carried out on spatially degraded data to objectively compare the proposed scheme to the most promising state-of-the-art image fusion methods, and on full-resolution image data to visually assess the performance of the proposed genetic image fusion method.

Garzelli, A., & F., N. (2006). PAN-sharpening of very high resolution multispectral images using genetic algorithms. INTERNATIONAL JOURNAL OF REMOTE SENSING, 27(15), 3273-3292 [10.1080/01431160600554991].

PAN-sharpening of very high resolution multispectral images using genetic algorithms

GARZELLI, ANDREA;
2006

Abstract

A novel image fusion method is presented, suitable for sharpening of multispectral (MS) images by means of a panchromatic (PAN) observation. The method is based on redundant multiresolution analysis (MRA); the MS bands expanded to the finer scale of the PAN band are sharpened by adding the spatial details from the MRA representation of the PAN data. As a direct, unconditioned injection of PAN details gives unsatisfactory results, a new injection model is proposed that provides the optimum injection by maximizing a global quality index of the fused product. To this aim, a real-valued genetic algorithm (GA) has been defined and tested on Quickbird data. The optimum GA injection is driven by an index function capable of measuring different types of possible distortions in the fused images. Fusion tests are carried out on spatially degraded data to objectively compare the proposed scheme to the most promising state-of-the-art image fusion methods, and on full-resolution image data to visually assess the performance of the proposed genetic image fusion method.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11365/24889
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