Abstract
The pansharpening (PS) of remote-sensing images aims to fuse a high-resolution panchromatic image with several low-resolution multispectral images for obtaining a high-resolution multispectral image. In this work, a two-stage PS model is proposed by integrating the ideas of component replacement and the variational method. The global sparse gradient of the panchromatic image is extracted by variational method, and the weight function is constructed by combining the gradient of multispectral image in which the global sparse gradient can provide more robust gradient information. Furthermore, we refine the results in order to reduce spatial and spectral distortions. Experimental results show that our method had high generalization ability for QuickBird, Gaofen-1, and WorldView-4 satellite data. Experimental results evaluated by seven metrics demonstrate that the proposed two-stage method enhanced spatial details subjective visual effects better than other state-of-the-art methods do. At the same time, in the process of quantitative evaluation, the method in this paper had high improvement compared with that other methods, and some of them can reach a maximal improvement of 60%.
Highlights
To overcome the trade-off between the spatial and spectral resolutions of remotesensing images, pansharpening (PS) fuses geometric details of a panchromatic (PAN) image with the spectral information of a multispectral (MS) image to obtain a high-resolutionMS image where the PAN image is the high-resolution image and the MS image is the low-resolution image
We developed a two-stage PS method based on the component substitution (CS) and variational models, namely, the global sparse gradient-based improved adaptive intensity hue saturation (IHS) (GIAIHS) method, and reduced the instability of fused image global information
For the relative average spectral error (RASE) index, the GIAIHS method improved by approximately 26% compared with the Gram–Schmidt adaptive (GSA) method
Summary
To overcome the trade-off between the spatial and spectral resolutions of remotesensing images, pansharpening (PS) fuses geometric details of a panchromatic (PAN) image with the spectral information of a multispectral (MS) image to obtain a high-resolutionMS image where the PAN image is the high-resolution image and the MS image is the low-resolution image. To overcome the trade-off between the spatial and spectral resolutions of remotesensing images, pansharpening (PS) fuses geometric details of a panchromatic (PAN) image with the spectral information of a multispectral (MS) image to obtain a high-resolution. The class of CS methods first projects the MS image into a new space on the basis of a spectral transformation and substitutes the matching spatial part by the PAN image, and obtains the fused MS image through the inverse projection. The main idea of the IHS method, as a classical work in CS methods, is to first perform IHS transform on an upsampled MS image, replace the I intensity part in the IHS space using the histogram-matched PAN image, and perform inverse IHS transform to obtain a fusion result.
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