Pansharpening is a technique used to reconstruct a high-resolution (HR) multispectral (MS) image by combining an HR panchromatic (PAN) image with a low-resolution MS image. In recent years, the detail-injection model has demonstrated excellent performance in pansharpening, thus receiving wide attention. Obtaining appropriate details is vital for the detail-injection model. Therefore, this article presents a detail optimization approach to obtain more precise high-frequency (HF) details for pansharpening. The proposed method comprises two steps. In the first step, we design a low rank fuzzy fusion model to fuse the HF details of the PAN and MS images. In this model, the high frequencies of the PAN and upsampled MS images are decomposed into low rank and sparse components, and the corresponding fusion rules are designed according to their characteristics. Because some details of the PAN image are replaced with those of the MS image, using them directly as injection details may result in redundant information or spatial distortion. To solve this problem and further optimize the details, in the second step, we construct an adaptive detail supplement model. Based on the similarity and correlation between the fused HF and the original HF of PAN image, the fused details are supplemented to obtain the final injection details. Experimental results on the IKONOS, Pleiades, QuickBird, and WorldView-2 datasets demonstrate that the proposed algorithm is better than the state-of-the-art methods in maintaining spectral information and improving spatial details.
The purpose of pansharpening is to fuse a multispectral (MS) image with a panchromatic (PAN) image to generate a high spatial-resolution multispectral (HRMS) image. However, the traditional pansharpening methods do not adequately take consideration of the information of MS images, resulting in inaccurate detail injection and spectral distortion in the pansharpened results. To solve this problem, a new pansharpening approach based on adaptive high-frequency fusion and injection coefficients optimization is proposed, which can obtain an accurate injected high-frequency component (HFC) and injection coefficients. First, we propose a multi-level sharpening model to enhance the spatial information of the MS image, and then extract the HFCs from the sharpened MS image and PAN image. Next, an adaptive fusion strategy is designed to obtain the accurate injected HFC by calculating the similarity and difference of the extracted HFCs. Regarding the injection coefficients, we propose injection coefficients optimization scheme based on the spatial and spectral relationship between the MS image and PAN image. Finally, the HRMS image is obtained through injecting the fused HFC into the upsampled MS image with the injection coefficients. Experiments with simulated and real data are performed on IKONOS and Pléiades datasets. Both subjective and objective results indicate that our method has better performance than state-of-the-art pansharpening approaches.
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