Aerial triangulation (AT) has reached outstanding progress in the last decades, and now fully automated solutions for nadir and oblique images are available. Usually, image correspondences (tie points) are found using hand-crafted methods, such as SIFT or its variants. But in the last years, there were many investigations and developments to promote the use of machine and deep learning solutions within the photogrammetric processing pipeline. The paper explores learning-based methods for the extraction of tie points in aerial image blocks. Image correspondences are used to perform aerial triangulation (AT) and successively generate dense point clouds. Two different datasets are used to compare conventional hand-crafted detector/descriptor methods with respect to learning-based methods. Accuracy analyses are performed using GCPs as well as ground truth LiDAR point clouds. Results confirm the potential of learning-based methods in finding reliable image correspondences in the aerial block, still showing space for improvements due to camera rotations.
Aerial triangulation with learning-based tie points
Remondino, F.;Morelli, L.;Stathopoulou, E.;
2022-01-01
Abstract
Aerial triangulation (AT) has reached outstanding progress in the last decades, and now fully automated solutions for nadir and oblique images are available. Usually, image correspondences (tie points) are found using hand-crafted methods, such as SIFT or its variants. But in the last years, there were many investigations and developments to promote the use of machine and deep learning solutions within the photogrammetric processing pipeline. The paper explores learning-based methods for the extraction of tie points in aerial image blocks. Image correspondences are used to perform aerial triangulation (AT) and successively generate dense point clouds. Two different datasets are used to compare conventional hand-crafted detector/descriptor methods with respect to learning-based methods. Accuracy analyses are performed using GCPs as well as ground truth LiDAR point clouds. Results confirm the potential of learning-based methods in finding reliable image correspondences in the aerial block, still showing space for improvements due to camera rotations.File | Dimensione | Formato | |
---|---|---|---|
isprs-archives-XLIII-B2-2022-77-2022.pdf
accesso aperto
Licenza:
Creative commons
Dimensione
2.95 MB
Formato
Adobe PDF
|
2.95 MB | Adobe PDF | Visualizza/Apri |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.