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.
@article{arxiv.2007.15417,
title = {Very Deep Super-Resolution of Remotely Sensed Images with Mean Square Error and Var-norm Estimators as Loss Functions},
author = {Antigoni Panagiotopoulou and Lazaros Grammatikopoulos and Eleni Charou and Emmanuel Bratsolis and Nicholas Madamopoulos and John Petrogonas},
journal= {arXiv preprint arXiv:2007.15417},
year = {2020}
}