Abstract
Pansharpening refers to a spatial–spectral contexts fusion procedure to produce high-quality multispectral (MS) images by retaining the fine spatial resolution of the panchromatic (PAN) images and the high spectral content of the MS images. This letter presents a novel end-to-end dual-branch deep learning-based fusion framework, exploiting the network to extract spatial and spectral contexts progressively in two separate branches level by level. For each level contexts extraction layer, a dual-branch weighted attentive fusion module is integrated to boost the important contexts aggregation and details injection while suppressing unimportant ones. Experimental results on two real datasets show that our method outperforms state-of-the-art methods in both objective metrics and image quality by visual appearance.
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