SSconv: Explicit Spectral-to-Spatial Convolution for Pansharpening

Pansharpening aims to fuse a high spatial resolution panchromatic (PAN) image and a low resolution multispectral (LR-MS) image to obtain a multispectral image with the same spatial resolution as the PAN image. Thanks to the flexible structure of convolution neural networks (CNNs), they have been successfully applied to the problem of pansharpening. However, most of the existing methods only simply feed the up-sampled LR-MS into the CNNs and ignore the spatial distortion caused by direct up-sampling. In this paper, we propose an explicit spectral-to-spatial convolution (SSconv) that aggregates spectral features into the spatial domain to perform the up-sampling operation, which can get better performance than the direct up-sampling. Furthermore, SSconv is embedded into a multiscale U-shaped convolution neural network (MUCNN) for fully utilizing the multispectral information of involved images. In particular, multiscale injection branch and mixed loss on cross-scale levels are employed to fuse pixel-wise image information. Benefiting from the distortion-free property of SSconv, the proposed MUCNN can generate state-of-the-art performance with a simple structure, both on reduced-resolution and full-resolution datasets acquired from WorldView-3 and GaoFen-2. Please find the code from the project page.