Abstract
Abstract. High-resolution satellite images have always been in high demand due to the greater detail and precision they offer, as well as the wide scope of the fields in which they could be applied; however, satellites in operation offering very high-resolution (VHR) images has experienced an important increase, but they remain as a smaller proportion against existing lower resolution (HR) satellites. Recent models of convolutional neural networks (CNN) are very suitable for applications with image processing, like resolution enhancement of images; but in order to obtain an acceptable result, it is important, not only to define the kind of CNN architecture but the reference set of images to train the model. Our work proposes an alternative to improve the spatial resolution of HR images obtained by Sentinel-2 satellite by using the VHR images from PeruSat1, a peruvian satellite, which serve as the reference for the super-resolution approach implementation based on a Generative Adversarial Network (GAN) model, as an alternative for obtaining VHR images. The VHR PeruSat-1 image dataset is used for the training process of the network. The results obtained were analyzed considering the Peak Signal to Noise Ratios (PSNR) and the Structural Similarity (SSIM). Finally, some visual outcomes, over a given testing dataset, are presented so the performance of the model could be analyzed as well.
Highlights
This section describes the experiments performed and the results that were achieved by our approach, which proposes to apply SR based on a Generative Adversarial Network (GAN) model to increase the spatial resolution of Sentinel-2 images with the reference of images from PeruSat-1 satellite
This work presents the application of Super-Resolution technique based on a generative adversarial network approach to improve the spatial resolution of Sentinel-2 (10m x pixel) satellite images in its blue, green, and red bands
We developed a GAN model based on the architecture proposed by (Ledig et al, 2017), which was trained with images from PeruSat-1 (2.8m x pixel) satellite
Summary
According to (Singh, 2018) there is an increasing demand for very high-resolution (VHR) images, which are the main resource to perform sophisticated applications in different research areas, such as computer vision, health, remote sensing, among others. Conventional models for super-resolution (SR) techniques, such as linear and cubic interpolations, splines, throws, and others, face some problems when processing fine details such as curves, edges, and textures, in images with highfrequency changes among their pixels (Singh, 2018). We take in consideration the model proposed by (Ledig et al, 2017), which besides integrates some pre-processing stages into the GAN network implementation so it can be able to process high-resolution (HR) satellite images
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