Abstract
Image fusion is a useful tool in integrating a high-resolution panchromaticimage (HRPI) with a low-resolution multispectral image (LRMI) to produce a highresolutionmultispectral image (HRMI). To date, many image fusion techniques have beendeveloped to try to improve the spatial resolution of the LRMI to that of the HRPI with itsspectral property reliably preserved. However, many studies have indicated that thereexists a trade- off between the spatial resolution improvement and the spectral propertypreservation of the LRMI, and it is difficult for the existing methods to do the best in bothaspects. Based on one minimization problem, this paper mathematically analyzes thetradeoff in fusing remote sensing images. In experiment, four fusion methods are evaluatedthrough expanded spectral angle mapper (ESAM). Results clearly prove that all the testedmethods have this property.
Highlights
With a combination of a set of observational constraints imposed by the acquisition system, detector specifications and satellite motion, among others, some satellite sensors supply low-resolution multispectral images (LRMIs) needed to identify features spectrally but not spatially, while other satellite sensors supply high-resolution panchromatic images (HRPIs) characterizing features spatially but not spectrally [1,2]
The HRPI; (c) The highresolution multispectral image (HRMI) produced by the IHS method; (d) The HRMIs produced by the OWD method; (e) The HRMIs produced by the AW method; (f) The HRMIs produced by the MAIM method
Based on the minimization problem (formula (2)), this paper mathematically demonstrates the tradeoff property during fusing the remote sensing images
Summary
With a combination of a set of observational constraints imposed by the acquisition system, detector specifications and satellite motion, among others, some satellite sensors supply low-resolution multispectral images (LRMIs) needed to identify features spectrally but not spatially, while other satellite sensors supply high-resolution panchromatic images (HRPIs) characterizing features spatially but not spectrally [1,2]. Various remote sensing image fusion methods have been proposed in the literature [1,4,5] These methods inject high frequency features from a HRPI into every LRMI trying to improve the spatial resolution of the LRMI to that of the HRPI with its spectral property reliably preserved. The objective is to obtain the HRMI that would be observed by a sensor with the same spectral response (i.e., spectral sensitivity and photon efficiency) as the multispectral sensors and the same spatial response (i.e., spatial detail and texture structure) as the panchromatic sensor [2] It is difficult for the existing methods to do the best in both aspects.
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