Chrominance-Aware Multi-Resolution Network for Aerial Remote Sensing Image Fusion

Spectral data obtained from upstream remote sensing tasks contain abundant complementary information. Infrared images are rich in radiative information, and visible images provide spatial details. Effective fusion of these two modalities improves the utilization of remote sensing data and provides a more comprehensive representation of target characteristics and texture details. The majority of current fusion methods focus primarily on intensity fusion between infrared and visible images. These methods ignore the chrominance information present in visible images and the interference introduced by infrared images on the color of fusion results. Consequently, the fused images exhibit inadequate color representation. To address these challenges, an infrared and visible image fusion method named Chrominance-Aware Multi-Resolution Network (CMNet) is proposed. CMNet integrates the Mamba module, which offers linear complexity and global awareness, into a U-Net framework to form the Multi-scale Spatial State Attention (MSSA) framework. Furthermore, the enhancement of the Mamba module through the design of the Chrominance-Enhanced Fusion (CEF) module leads to better color and detail representation in the fused image. Extensive experimental results show that the CMNet method delivers better performance compared to existing fusion methods across various evaluation metrics.