Deep Learning based Super Resolution of Aerial and Satellite Imagery

Super-resolution is the process of creating high-resolution (HR) images from low-resolution (LR) images. Single Image Super Resolution (SISR) is challenging because high-frequency image content typically cannot be recovered from the low-resolution image and the absence of high-frequency information thus limits the quality of the HR image. Furthermore, SISR is an ill-posed problem because a LR image can yield several possible high-resolution images. To address this issue, numerous techniques have been proposed but recently deep learning based methods have become popular. Convolutional Neural Network (CNN) approaches to deep learning have shown great success in numerous computer vision tasks. Therefore, it is worthwhile to explore CNN-based approaches to address this challenging problem. This paper presents a deep learning based super resolution (DLSR) approach to find a HR image from its LR counterpart by learning the mapping between them. This mapping is possible because LR and HR images have similar image contents and differ primarily in high-frequency details. In addition, DLSR utilizes residual learning strategy where the network learns to estimate a residual image. DLSR is applied to both aerial and satellite imagery and resulting estimates are compared against the traditional methods using metrics such as Peak Signal to Noise Ratio (PSNR), Structure Similarity Index Metric (SSIM), and Naturalness Image Quality Evaluator (NIQE) also called perceptual quality index. Results obtained depict that DLSR outperform the traditional approaches.