Triple-loss driven generative adversarial network for pansharpening

Pansharpening aims at fusing a panchromatic (PAN) image and a low-resolution multispectral (LRMS) image into a high-resolution multispectral (HRMS) image. In recent years, GAN-based pansharpening methods have achieved excellent results, but they suffer from inadequate feature preservation and unstable training. To address these issues, a novel GAN-based model named TriLossGAN is proposed. This method constructs three loss components with the help of the generator and the dual-discriminator, which are calculated in both the original spatial domain and the transform domain to better preserve high-frequency and low-frequency information in the fused image. Additionally, a new training strategy is designed to stabilize the training process. In extensive experiments, the proposed method achieved satisfactory results on three datasets with QNR values of 0.9584 on GaoFen-2, 0.9601 on QuickBird, and 0.9138 on WorldView-3. Qualitative and quantitative comparisons demonstrate that TriLossGAN outperforms other state-of-the-art methods. This method constructs three loss components with the help of the generator and the dual-discriminator, which are calculated in both the original spatial domain and the transform domain to better preserve high-frequency and low-frequency information in the fused image. Additionally, a new training strategy is designed to stabilize the training process.image