pansharpening

registration

pan-sharpening

fusion_87487

spatiotemporal fusion

fusion_87513

ms images

multispectral images

hrms

spatial detail

Information Fusion

Pan-sharpening in remote sensing image fusion refers to obtaining multi-spectral images of high-resolution by fusing panchromatic images and multi-spectral images of low-resolution. Recently, convolution neural network (CNN)-based pan-sharpening methods have achieved the state-of-the-art performance. Even though, two problems still remain. On the one hand, the existing CNN-based strategies require supervision, where the low-resolution multi-spectral image is obtained by simply blurring and down-sampling the high-resolution one. On the other hand, they typically ignore rich spatial information of panchromatic images . To address these issues, we propose a novel unsupervised framework for pan-sharpening based on a generative adversarial network, termed as Pan-GAN, which does not rely on the so-called ground-truth during network training. In our method, the generator separately establishes the adversarial games with the spectral discriminator and the spatial discriminator , so as to preserve the rich spectral information of multi-spectral images and the spatial information of panchromatic images. Extensive experiments are conducted to demonstrate the effectiveness of the proposed Pan-GAN compared with other state-of-the-art pan-sharpening approaches. Our Pan-GAN has shown promising performance in terms of qualitative visual effects and quantitative evaluation metrics.

Pan-GAN: An unsupervised pan-sharpening method for remote sensing image fusion