GAF-Net: Improving the Performance of Remote Sensing Image Fusion using Novel Global Self and Cross Attention Learning

The notion of self and cross-attention learning has been found to substantially boost the performance of remote sensing (RS) image fusion. However, while the self-attention models fail to incorporate the global context due to the limited size of the receptive fields, cross-attention learning may generate ambiguous features as the feature extractors for all the modalities are jointly trained. This results in the generation of redundant multi-modal features, thus limiting the fusion performance. To address these issues, we propose a novel fusion architecture called Global Attention based Fusion Network (GAF-Net), equipped with novel self and cross-attention learning techniques. We introduce the within-modality feature refinement module through global spectral-spatial attention learning using the query-key-value processing where both the global spatial and channel contexts are used to generate two channel attention masks. Since it is non-trivial to generate the cross-attention from within the fusion network, we propose to leverage two auxiliary tasks of modality-specific classification to produce highly discriminative cross-attention masks. Finally, to ensure non-redundancy, we propose to penalize the high correlation between attended modality-specific features. Our extensive experiments on five benchmark datasets, including optical, multispectral (MS), hyperspectral (HSI), light detection and ranging (LiDAR), synthetic aperture radar (SAR), and audio modalities establish the superiority of GAF-Net concerning the literature.