Pansharpening via Triplet Attention Network With Information Interaction

Abstract

Pansharpening aims to obtain high spatial resolution multispectral (MS) images by fusing the spatial and spectral information in low spatial resolution (LR) MS and panchromatic (PAN) images. Recently, deep neural network (DNN) based pansharpening methods have been advanced extensively. Although most DNN-based methods show good performance, it is difficult for them to preserve the spatial details in the fused image. In this article, we propose a new pansharpening method based on a triplet attention network with information interaction to efficiently enhance the spatial and spectral information in the fused image. First, different attention mechanisms are designed to model the spatial and spectral feature properties in LR MS and PAN images. Then, the complementarity among different feature maps is enhanced by information interaction, which promotes the compatibility of features from subnetworks. Finally, we utilize a graph attention module to capture the similarity within feature maps. According to the graph, the informative feature maps are selected to provide more details for the reconstruction of the fused image. Extensive experiments on QuickBird and GeoEye-1 satellite datasets show that the proposed method can produce competitive fused images when compared with some state-of-the-art methods.