Investigating the performance of super-resolved remote sensing images on coastline segmentation with deep learning-based methods

Abstract

The use of satellite imagery in critical areas, such as environmental monitoring and natural disaster management, is becoming increasingly important. Applications like monitoring coastal areas, detecting coastal erosion, and tracking land use changes demand high accuracy and detailed analysis. Traditional methods for coastline segmentation are often limited by the low resolution (LR) and high complexity of satellite imagery. To address this challenge, Super Resolution (SR) algorithms are employed to enhance the resolution of satellite images, which is particularly beneficial when examining areas with intricate structures, such as coastlines. In this context, the integration of SR and segmentation techniques presents an innovative approach to achieving greater accuracy and efficiency in satellite image analysis. In this study, the resolution of satellite images was enhanced using the Super Resolution Generative Adversarial Networks (SRGAN) model. Thanks to the flexible architecture of the SRGAN model, it was successfully adapted to work with satellite images, yielding satisfactory results. Coastal segmentation was performed using low-resolution, super-resolved, and high-resolution Gokturk-1 (GT-1) satellite images, employing U-net, LinkNet, and DeepLabV3+ segmentation models for comparison. The results indicated that increment in image resolution significantly affects segmentation success. Additionally, better performance in coastline segmentation was achieved with U-net and LinkNet models. Although the DeepLabV3+ model is effective for segmentation, it tends to capture less detail compared to the other two models. Overall, the combination of SRGAN and the LinkNet segmentation model produced results that were closest to reality.