SWPanGAN: A hybrid generative adversarial network for pansharpening

Pansharpening is a vital technique in remote sensing that combines a low-resolution multi-spectral image with its corresponding panchromatic image to obtain a high-resolution multi-spectral image. Despite its potential benefits, the challenge lies in extracting features from the source images and eliminating artefacts in the fused images. In response to the challenge, a hybrid generative adversarial network-based model, termed SWPanGAN, is proposed. For better feature extraction, the conventional convolution neural network is replaced with a Swin transformer in the generator, which provides the generator with the ability to model long-range dependencies. Additionally, to suppress artefacts, a wavelet-based discriminator is proposed for effectively distinguishing the frequency discrepancy. With these modifications, both the generator and discriminator networks of SWPanGAN are enhanced. Extensive experiments illustrate that our SWPanGAN can generate high-quality pansharpening images and surpass other state-of-the-art methods. This paper introduces SWPanGAN, a novel pansharpening method that combines Swin Transformer architectures with discrete wavelet transform in a GAN framework. The approach effectively fuses multimodal data and detects artefacts, offering a significant advancement in image fusion and pansharpening. image