Multispectral image fusion method based on edge chromatic aberration

Abstract. In multispectral image fusion, edge distortion, spectral loss, and geometric mismatch seriously affect the fusion accuracy, especially in complex road scenes with shadows or occlusions. Multispectral image fusion aims to preserve surface details and spectral data. In order to solve the problems of edge distortion and spectral loss in image fusion, this paper proposes a multispectral and hyperspectral fusion method based on edge chromatic aberration. Swin Transformer is used for multi-scale feature extraction, and the GRDB module is added to preserve edge details, which improves the clarity and accuracy of diseased edges in road scene fusion images. In addition, saliency weight mapping can identify and highlight key disease areas, ensuring that they are prominent in the fused image.
Experiments show that the multispectral image fusion method based on edge color difference significantly improves the performance of road damage analysis on the self-built BUCEA-MS-Road-Damage dataset: the edge IoU in the detection task is increased to 80.1% (+1.3%), and the target detection accuracy is 92.3% (+3.6%); the accuracy and recall of the classification task are increased to 91.3% (+3.0%) and 89.8% (+3.0%) respectively; the Dice coefficient of the segmentation task is 83.3% (+3.0%). In the cross-sensor test, the fusion result is still robust (SSIM=0.93, SAM=2.7°), and the edge color difference index (ECD-Index=6.3) is 25.9% lower than the baseline. This method effectively solves the problems of multi-scale feature extraction and texture distortion of cracks through adaptive color difference correction and spectral consistency constraints, providing high-precision data support for intelligent road maintenance.