Deep learning approaches for linear feature segmentation in remote sensing and astronomical imagery

Abstract

1 online resource (101 pages) : illustrations (some colour), charts (some colour), graphs (some colour)Includes abstract.Includes bibliographical references (pages 76-101).Linear features, defined by their elongated geometry, are critical in domains such as transportation (roads, railways), environmental monitoring (rivers, pipelines), and astronomy (satellite streaks). Accurate segmentation of these features supports automated analysis and decision-making. This research focuses first on road infrastructure, introducing an innovative framework for health assessment using high-resolution satellite imagery, Gray Level Co-occurrence Matrix (GLCM) texture features, and a cyclic reinforcement learning agent. The model dynamically predicts Pavement Condition Index (PCI) and International Roughness Index (IRI), achieving low error and strong generalization on Alberta’s Open Road dataset, offering a cost effective alternative to manual or sensor based methods. Beyond transportation, we address segmentation of elongated satellite streaks in astronomy using an enhanced U-Net trained on the SatStreaks Dataset, achieving superior accuracy and precision compared to baseline models. Together, these contributions demonstrate the versatility of deep learning and reinforcement learning in detecting, segmenting, and assessing linear features across diverse imaging domains