Generative Adversarial Networks (GANs) are one of the most popular Machine Learning algorithms developed in recent times, and are a class of neural networks that are used in unsupervised machine learning. The advantage of unsupervised machine learning approaches such as GANs is that they do not need a large amount of labeled data, which is costly and time consuming. GANs may be used in a variety of applications, including image synthesis, semantic image editing, style transfer, image super-resolution and classification. In this work, GANs are utilized to solve the single image super-resolution problem. This approach in literature is referred to as super resolution GANs (SRGAN), and employs a perceptual loss function which consists of an adversarial loss and a content loss. The adversarial loss pushes the solution to the natural image manifold using a discriminator network that is trained to differentiate between the super-resolved images and the original photo-realistic images, and the content loss is motivated by the perceptual similarity and not the similarity in the pixel space. This paper presents implementation of SRGAN using Deep convolution network applied to both the aerial and satellite imagery of the aircrafts. The results thus obtained are compared with traditional super resolution methods. The resulting estimates of SRGAN are compared against the traditional methods using peak signal to noise ratio (PSNR) and structure similarity index metric (SSIM). The PSNR and SSIM of SRGAN estimates are similar to traditional method such as Bicubic interpolation but traditional methods are often lacking high-frequency details and are perceptually unsatisfying in the sense that they fail to match the fidelity expected at the higher resolution.