Using Deep Learning–Extracted Road Networks for More Accurate Small Satellite Geometric Correction

Abstract

Abstract. In recent decades, many different satellites for Earth observation have been launched. They produce large amounts of data that, if properly preprocessed, can be used in many applications. A rapidly growing portion of these data comes from small satellites, which remain underused by scientists and entrepreneurs. However, greater utilisation can be ensured by producing images with positional accuracy of at least two pixels, which is necessary for their reliable use. This paper presents the upgraded version of the geometric correction module of the STORM processing chain. The module can automatically orthorectify images from the NEMO-HD small satellite, which, like other small satellites, in principle has a lower signal-to-noise ratio (SNR) and higher radiometric variability. It automatically extracts ground control points (GCPs) by matching freely available reference vector roads and reference images to roads extracted from the satellite image using a deep learning method trained on PlanetScope and NEMO-HD imagery. The performance of the geometric correction module was evaluated using three images acquired over Slovenia. The road extraction method can achieve an F1-score of approximately 60%. The tests demonstrated that automatic GCP extraction based on roads detected by a deep learning method is a viable approach for achieving geometric model accuracies of two pixels or less at independent check points when using small satellite imagery.