  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.
