Abstract. Satellite imagery enables large-scale remote sensing applications by providing frequent and large-scale coverage. However, its limited spatial resolution often restricts the use of satellite images in tasks that require detailed, fine-scale information. In contrast, aerial images offer a much higher spatial resolution, allowing the extraction of fine-grained features, but typically cover smaller, more localized areas. In this work, we investigate whether super-resolution (SR) methods can bridge the gap between aerial and high-resolution satellite imagery, enabling a label-free model transfer, meaning without fine-tuning our model with additional manual annotations. The idea is to enhance the spatial resolution of high-resolution satellite images, allowing models trained on aerial data to be directly applied to satellite images. Towards this goal, a state-of-the-art SR algorithm is used to upscale three high-resolution satellite images, matching the resolution of the aerial training data. Then, a segmentation network trained on an aerial image dataset is applied to segment roads and parking areas in the super-resolved satellite images. The approach is evaluated on an annotated dataset and compared to the results in the original satellite images. Additionally, we investigate its performance on a low-resolution aerial image. Our results demonstrate that SR facilitates the utilization of models trained on aerial image datasets for large-scale satellite applications without requiring new labels.
N. Merkle, C. Henry, S. Jangir et al.· ISPRS Annals of the Photogra...· 0 citations
Abstract. Accurate image matching is essential for the precise orientation of airborne imagery, yet modern feature matchers are rarely evaluated on real aerial data with great temporal, seasonal, and radiometric changes. For this study, we introduce the AerialRefMatch dataset, which comprises 51 challenging aerial images and corresponding true-ortho reference data. We benchmark classical and deep learning–based matching algorithms on AerialRefMatch, considering two scenarios: matching original images and matching approx-orthorectified images generated using GNSS/IMU orientations. For each method, image-based ground control points are derived and used for single-image pose estimation; accuracy is assessed via independent checkpoints. Results show that directly matching on original images is very difficult: fewer than 14% of images can be oriented with pixel-level accuracy. When approxorthorectification is used, performance improves substantially. JamMa, SIFT, and SuperPoint+LightGlue achieve pixel-level accuracy for up to 30% of images, with JamMa being most robust on difficult cases and SIFT-based variants being more precise on the easier ones. Deep detector-free models such as ELoFTR and RoMa are less accurate but more robust to the original images than other models. Overall, state-of-the-art deep learning-based matchers still struggle with large rotations, scale differences, and semantic differences, and strongly benefit from prior image orientation knowledge and lack sub-pixel precision. The AerialRefMatch dataset can be downloaded here: https://www.dlr.de/en/eoc/aerial-ref-match
Pablo d'Angelo, F. Kurz, Alaa Eddine Ben Zekri et al.· ISPRS Annals of the Photogra...· 0 citations