Very Deep Super-Resolution of Remotely Sensed Images with Mean Square Error and Var-norm Estimators as Loss Functions
This work addresses improving spatial resolution for remote sensing applications, but it is incremental as it builds on an existing VDSR method with new loss functions and datasets.
The paper tackled super-resolution of remotely sensed images at scale factor 4 by retraining a very deep super-resolution network with Sentinel-2 and drone images, proposing a novel Var-norm estimator loss function, resulting in RS-VDSR outperforming VDSR by up to 3.16 dB PSNR in Sentinel-2 images.
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.