An Efficient Pansharpening Approach Based on Texture Correction and Detail Refinement

Abstract

Pansharpening aims at fusing a multispectral (MS) image and panchromatic (PAN) image to obtain a high spatial resolution multispectral (HRMS) image. To obtain accurate details and reduce spectral distortion, this letter proposes an efficient pansharpening approach based on texture correction (TC) and detail refinement. First, a TC model is constructed based on spatial and spectral fidelity constraints to obtain a texture image that is highly correlated with the MS image. Second, a detail acquisition model is proposed by the consecutive parameter regression to adaptively refine the extracted details. Finally, the extracted details are injected into the up-sampled MS (UPMS) image to obtain the fused HRMS image. Experimental results demonstrate that the proposed method can obtain the high-quality results with high efficiency.