Satellite and Aerial Image Restoration Using Deep Reinforcement Learning

Abstract

In this paper, we present a deep reinforcement learning-based method for effectively denoising satellite and aerial imagery data. Noise of various kinds and with varying noise levels contaminates satellite imagery data. The image’s quality and readability suffer when there is noise present. Therefore, it is crucial to create a network that can effectively and efficiently remove noise from the image while also preserving its quality and signal components. This paper evaluates the denoising capabilities of the deep reinforcement learning system. The proposed network is trained using the training set from the “dataset of object detection in aerial images (DOTA) dataset,” and its hyperparameters were adjusted for optimum performance. The training set from the aforementioned dataset was used to train the proposed network. The trained network was given the test set of unseen images for denoising. Statistical denoising, a common denoising technique, was used on the test dataset, and the outcomes were assessed. The same unseen images were also given to existing CNN-based denoising algorithms like denoising using CNN (DnCNN), U-shaped DnCNN (UDnCNN), and dilated U-shaped DnCNN (DUDnCNN), designed specifically for image denoising. Runtime and structural similarity index (SSIM) as well as peak signal-to-noise ratio have both been used as evaluation metrics to compare the effectiveness of various approaches. It is discovered that, when comparing the performance of various systems, the suggested system outperforms both statistical- and CNN-based denoising in terms of the evaluation metrics, PSNR and SSIM.