Triple‐loss driven generative adversarial network for pansharpening

Abstract

Abstract 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.

Keywords

• image fusion
• neural nets
• remote sensing