Complementary Fusion Network Based on Frequency Hybrid Attention for Pansharpening

Abstract

Pansharpening is a feasible way to obtain the high-resolution (HR) multispectral (MS) images by using panchromatic (PAN) images to sharpen low-resolution MS images. Despite its great advances, most existing pansharpening methods neglect the importance of integrating local and non-local characteristics of images, resulting in the imbalance of spatial and spectral distribution. In this paper, we propose a complementary fusion network (CFNet) based on frequency hybrid attention mechanism for pansharpening. By introducing the frequency transformation and the deformable cross-attention, our model takes image-wide receptive field into consideration to explore global feature learning. Combined with the convolutional layers with local receptive field, CFNet can well capture local and non-local features. Experimental results demonstrate that the proposed method outperforms the comparison methods in terms of visual and quantitative qualities.