CIGformer: Leveraging Continuous Information Guidance in Transformer-Based Pansharpening

Pansharpening has been a critical area of interest. However, current methods often fail to fully leverage the correlation between Panchromatic (PAN) and Multispectral (MS) images, resulting in spatial and spectral information loss. Additionally, uniform processing across different scales can cause information interference. We introduce CIGformer, a multi-scale fusion network utilizing a continuous information guidance mechanism to tackle these issues. Our approach introduces an Intensity Substitute Block (ISB) to separate shared and unique features of PAN and MS, setting the stage for subsequent guidance. The core component, an Information Guidance Block (IGB) based on Transformer architecture, ensures adaptive retention of unique information while utilizing shared information. Furthermore, our multi-level Encoder-Decoder bidirectional pyramid structure minimizes multi-scale information mixing, with IGB applied at each encoder level for optimal information use. A consistency loss function is also introduced to enhance training by assessing unique information retention. Our method significantly enhances the efficiency of using PAN and MS images through distinctive information guidance, as demonstrated by experiments on the GaoFen-1, GaoFen-2, and WorldView-3 datasets.