计算机科学 ›› 2022, Vol. 49 ›› Issue (2): 116-122.doi: 10.11896/jsjkx.210700095
陈贵强, 何军
CHEN Gui-qiang, HE Jun
摘要： 在遥感图像超分辨率重建领域,大部分数据集缺少成对的图像用于训练,当前的方法主要是通过双三次插值的方式来获取低分辨率图像,因退化模型过于理想化导致在处理真实低分辨率遥感图像时效果较差,基于此,文中提出了一种自然场景下真实遥感图像的超分辨率重建算法。针对缺少成对图像的数据集的问题,构建了一种更合理的退化模型,将成像过程中的退化先验知识(如模糊、噪声、降采样等)随机混洗,以模拟自然场景下低分辨遥感图像的生成过程,生成逼真的低分辨率图像用于训练;同时,改进了一种基于生成对抗网络的超分辨率重建算法,在生成网络中引入注意力机制,以增强遥感图像纹理细节。在UC Merced数据集上,所提方法的PSNR/SSIM较ESRGAN和RCAN分别提升了1.407 1 dB/0.067 2,0.821 1 dB/0.023 5;在真实遥感数据集Alsat2B上,所提方法在3种地形上的平均PSNR/SSIM较基线模型提升了1.758 4 dB/0.048 5,重建图像视觉效果也优于基线模型,从而验证了退化模型和重建模型的有效性。
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