Multiscale Integration Network With Quaternion Convolution for Pansharpening

Abstract

In this letter, we proposed a multiscale integration network with quaternion convolution (MQ-Net) for the fusion of low spatial resolution multispectral (LRMS) and panchromatic (PAN) images. In this network, LRMS and PAN images are resampled at different scales and fed into feature fusion modules (FFMs) to merge the spatial and spectral information among them. Then, multiscale feature enhancement modules (MFEMs) are designed to sufficiently learn the spatial and spectral information at different scales. Meanwhile, we employ a quaternion convolution module (QCM) to better capture the dependencies within spectral bands of LRMS images. Then, the quaternion features are introduced into MFEMs for efficient feature enhancement. Finally, all information from different scales is integrated for the reconstruction of high LRMS images. Reduced- and full-resolution experiments are performed on GeoEye-1 and WorldView-2 satellite datasets. Compared to some state-of-the-art pansharpening methods, the proposed MQ-Net obtains better results in terms of qualitative and quantitative evaluations. The code is available at https://github.com/RSMagneto/MQ-Net .