摘要
现有遥感图像锐化方法普遍采用Wald协议,导致重建图像存在空间纹理细节和颜色模糊、边缘过于平滑的问题。针对该问题,提出基于生成对抗网络(Generative Adversarial Networks,GAN)的遥感图像锐化方法PAN-GAN。该方法将多光谱图像作为参考图像,使用灰度化的参考图像模拟全色图像,并与模糊化的参考图像共同作为生成器输入,由生成器分别提取前者的纹理细节特征和后者的光谱特征并进行融合重构;引入感知损失,联合对抗损失和像素损失共同优化重构图像,使重构图像具有更加逼近参考图像的光谱和纹理细节特征。在QuickBird,GaoFen-2和WorldView-2这3个遥感卫星的图像数据集上进行实验,结果表明:与常用方法相比,使用PAN-GAN得到的重构图像具有更加逼真的光谱和空间纹理细节;使用灰度化的参考图像能够显著提升原有方法的性能并且平均灰度化提升最为明显;感知损失的引入进一步优化了重构结果,验证了所提方法的有效性。
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.
作者
闫艳
隋毅
司建伟
YAN Yan;SUI Yi;SI Jianwei(College of Computer Science and Technology,Qingdao University,Qingdao,Shandong 266071,China;Faculty of Information Science and Engineering,Ocean University of China,Qingdao,Shandong 266100,China)
出处
《计算机科学》
CSCD
北大核心
2023年第8期133-141,共9页
Computer Science
基金
国家自然科学基金青年科学基金(41706198)。
关键词
遥感图像
锐化
对抗性生成网络
感知特征
Remote sensing images
Pan-sharpening
Generative adversarial networks
Perceptual features
