A MULTI-LEVEL SUPERVISED NETWORK FOR PANSHARPENING TO REDUCE COLOR DISTORTION

Due to the inherent limitations of satellites, obtaining high-resolution multispectral (MS) images directly poses a challenge. Consequently, several pansharpening methods have been proposed to fuse panchromatic (Pan) images with MS images in order to generate high-resolution MS images. However, the resulting fused images often suffer from color distortion. To address this issue, we developed a multi-level supervised network aimed at minimizing color distortion. Our approach disassembled the pansharpening method into two models: an image generation module and a color optimization module. The image generation module was responsible for producing an initial fused image with rich texture, while the color optimization module focused on correcting the grey distribution of each band to achieve a high-fidelity fused image. Through experiments conducted on GaoFen-2, we have demonstrated significant improvements in reducing color distortion using our proposed method.