Scale Invariant SuperResolutions Methods with Application to InSAR Images SuperResolution is the process of generating highresolution (HR) images from a lowresolution (LR) ones. In learningbased SR algorithms, artificial neural networks (ANN) are used. This is achieved by training the network using HR and LR image pairs and use this network later to create new HR images from LR ones. Our work postulates that the scaling process is invariant across scales. Thus, a model trained at lower scales can be used to reconstruct higher resolution images when the ground truth is not available to train the model. We call this approach Scale Invariant SuperResolution (SINV) We evaluated SINV using different datasets, and with different upscaling factors11The upscaling factor is the factor by which the image resolution is increased. and showed that it outperforms conventional approaches. We have applied SINV to processing InSAR images.

Deep Learning for Remote Sensing Image SuperResolution The aim of image superResolution (SR) is to enhance image resolution while still retain the integrity of the original image. There are many ongoing types of research on image superresolution for natural images, but any a few on remote sensing images. In this paper, we proposed deep learningbased image superresolution techniques, including convolutional neural network (CNN) and generative adversarial network (GAN) to enhance the resolution of remote sensing images by a factor 4. In CNN, it learns an end to end mapping from lowresolution image to highresolution image whereas, in GAN, the model learns the mapping guided by the GAN loss and gives the sharper appearance in highresolution images. Our experimental results show that visually GAN models perform well but are inferior to other models in terms of image quality metrics, whereas quantitatively CNN models outperform other superresolution models.