Cross-Interaction Kernel Attention Network for Pansharpening

The aim of pansharpening is to fuse panchromatic (PAN) images and the corresponding low-resolution multispectral (LRMS) images to generate high-resolution multispectral (HRMS) images. In recent years, convolutional neural network (CNN)-based methods have obtained excellent performance in this field. In order to break through the limitations of standard convolution, the dynamic convolution is also proposed for pansharpening. However, the existing dynamic convolution ignores the context along channel dimension and does not consider the mutual enhancement between spatial and spectral information. In this letter, we propose a novel cross-interaction kernel attention network (CIKANet). The proposed model consists of two branches to extract spectral and spatial information. In particular, spatial and spectral kernel attention modules that can dynamically scale the parameters of convolutional kernels are applied. To strengthen the interactions between branches and obtain complementary information, we adopt a cross kernel attention structure. A series of experiments conducted on the WV3 and QB datasets suggest that CIKANet outperforms other state-of-the-art (SOTA) models visually and quantitatively.