Remote Sensing Image Pan-sharpening Method Based on Generative Adversarial Network

Abstract

Remote sensing image pan-sharpening methods are generally based on Wald protocol,resulting in blurred texture details,colors and ambiguous boundaries of the reconstructed images.To solve the problem,a remote sensing image pan-sharpening method based on generative adversarial networks(GAN),PAN-GAN,is proposed in this paper.The multispectral image is employed as the reference image.The grayscale reference image is applied to simulate the panchromatic image and the blurred reference image is adpoted as input of the generator.The generator extracts the texture details of the grayscale reference image and spectral features of the blurred reference image for the fusion reconstruction.Meanwhile,the perceptual loss is introduced to optimize the reconstruction results with adversarial loss and pixel loss,so that the reconstructed images have spectral and texture detail features closer to the reference image.Experiments are carried out on the datasets of three remote sensing satellites including QuickBird,GaoFen-2 and WorldView-2.The results show that the reconstructed images obtained by PAN-GAN have more realistic spectral and spatial texture details compared with common methods.The usage of grayscale reference images can significantly improve the performance of the original method,and the average grayscale improvement is the most obvious.The perceptual loss can further optimize the reconstruction results and verify the effectiveness of the proposed method.