EBIFusion: An Enhancement Module Based on Error Backtracking for Remote Sensing Image Fusion

High-resolution multispectral images are of great value in various fields. However, the physical limitations of satellite sensors hinder the simultaneous acquisition of high spatial resolution and high spectral resolution images. Deep learning has become a powerful tool for remote sensing image fusion, but its full potential has not been fully utilized. In order to maximize the quality of the high-resolution multispectral images generated by the improved model, this paper proposes a module called EBIFusion, which introduces an error backtracking mechanism to improve fusion performance. The module uses the intermediate results of the deep learning model to capture the information lost during the high-resolution image generation process, thereby guiding the optimization of model training. The experimental results on the GF-6 dataset show that the QNR index is increased by 2.22% after the introduction of the module. In addition, the spatial and spectral quality has been continuously improved on multiple datasets, including QuickBird and GF-2. The optimized models, such as PSGAN, PNN, GPPNN, and UCGAN, show stronger spatial details and spectral fidelity. The main contribution of this paper is to propose an error backtracking framework for recovering and compensating for the lost information in the process of remote sensing image fusion based on deep learning. Based on this, a lightweight and model-independent enhancement module EBIFusion is designed, which can be integrated into different deep learning fusion architectures. At the same time, the generalization ability of the module in multiple datasets and network paradigms is verified. In summary, the error backtracking module enhances the quality of the generated high-resolution multispectral images. In addition, it is not limited to specific models and data and can be used as the basis for a versatile and effective optimization component to improve the availability of high-resolution multispectral images.