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Fusion of high spatial resolution (HSR) multispectral (MS) and panchromatic (PAN) images has become a research focus with the development of HSR remote sensing technology. In order to reduce the spectral distortions of fused images, current image fusion methods focus on optimizing the approach used to extract spatial details from the PAN band, or on the optimization of the models employed during the injection of spatial details into the MS bands. Due to the resolution difference between the MS and PAN images, there is a large amount of mixed pixels (MPs) existing in the upsampled MS images. The fused versions of these MPs remain mixed, although they may correspond to pure PAN pixels. This is one of the reasons for spectral distortions of fusion products. However, few methods consider spectral distortions introduced by the mixed fused spectra of MPs. In this paper, an image fusion method based on image segmentation was proposed to improve the fused spectra of MPs. The MPs were identified and then fused to be as close as possible to the spectra of pure pixels, in order to reduce spectral distortions caused by fused MPs and improve the quality of fused products. A fusion experiment, using three HSR datasets recorded by WorldView-2, WorldView-3 and GeoEye-1, respectively, was implemented to compare the proposed method with several other state-of-the-art fusion methods, such as haze- and ratio-based (HR), adaptive Gram–Schmidt (GSA) and smoothing filter-based intensity modulation (SFIM). Fused products generated at the original and degraded scales were assessed using several widely-used quantitative quality indexes. Visual inspection was also employed to compare the fused images produced using the original datasets. It was demonstrated that the proposed method offers the lowest spectral distortions and more sharpened boundaries between different image objects than other methods, especially for boundaries between vegetation and non-vegetation objects.

An Image Fusion Method Based on Image Segmentation for High-Resolution Remotely-Sensed Imagery