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Pan-Sharpening Based on Background Prior Saliency and Joint Sparse Detail Extraction

For remote sensing images, the spectral and spatial quality requirements vary across regions. Foreground objects need high spatial quality, while background regions require high spectral fidelity for the successive processing. Saliency analysis is an effective tool for distinguishing and achieving these requirements. Thus, we propose a pan-sharpening method based on background prior saliency and joint sparse detail extraction for remote sensing images. First, aimed at the characteristics of remote sensing image fusion, a saliency analysis method based on the foreground distribution and background prior is proposed to produce a regulation factor, which can reflect the different spatial and spectral information requirements of the foreground and background. Then, we propose a detail extraction and fusion method based on the guided filter and sparse representation. We extract spatial details not only from panchromatic (PAN) images but also from multispectral (MS) images and fuse them by maximizing the sparse coefficient strategy to reduce instabilities and dissimilarities. Finally, the regulation factor is used to regulate the detail injection in the pan-sharpening process. Our method can satisfy the various spatial and spectral resolution requirements for different regions more accurately. Compared to other state-of-art methods, both the visual and quantitative results reveal that our method has a better performance at improving the spatial quality and preserving the spectral fidelity.