论文标题
远程感知的图像非常深的超级分辨率，均为正方形误差和VAR-NORM估计器作为损耗函数
Very Deep Super-Resolution of Remotely Sensed Images with Mean Square Error and Var-norm Estimators as Loss Functions
论文作者
论文摘要
在这项工作中，提出了非常深的超级分辨率（VDSR）方法，用于改善对量表因子4的远程传感（RS）图像的空间分辨率。VDSR NET通过Sentinel-2图像重新训练，并使用无人机Aero Aero Orthophoto图像进行了训练，因此分别成为RS-VDSR和Aero-VDSR。在重新训练和预测期间，在卷积神经网络的回归层中提出了一种新型的损失函数，即VAR-NORM估计量。根据数值和光学比较，在使用RS图像预测期间，提出的NETS RS-VDSR和AERO-VDSR可以胜过VDSR。在Sentinel-2图像中，RS-VDSR的表现优于VDSR高达3.16 dB。
In this work, very deep super-resolution (VDSR) method is presented for improving the spatial resolution of remotely sensed (RS) images for scale factor 4. The VDSR net is re-trained with Sentinel-2 images and with drone aero orthophoto images, thus becomes RS-VDSR and Aero-VDSR, respectively. A novel loss function, the Var-norm estimator, is proposed in the regression layer of the convolutional neural network during re-training and prediction. According to numerical and optical comparisons, the proposed nets RS-VDSR and Aero-VDSR can outperform VDSR during prediction with RS images. RS-VDSR outperforms VDSR up to 3.16 dB in terms of PSNR in Sentinel-2 images.
