Correcting Imprecise Object Locations for Training Object Detectors in Remote Sensing Applications - Maximilian Bernhard; Matthias Schubert (Remote Sensing, 2021)
Object detection on aerial and satellite imagery is an important tool for image analysis in remote sensing and has many areas of application. As modern object detectors require accurate annotations for training, manual and labor-intensive labeling is necessary. In situations where GPS coordinates for the objects of interest are already available, there is potential to avoid the cumbersome annotation process. Unfortunately, GPS coordinates are often not well-aligned with georectified imagery. These spatial errors can be seen as noise regarding the object locations, which may critically harm the training of object detectors and, ultimately, limit their practical applicability. To overcome this issue, we propose a co-correction technique that allows us to robustly train a neural network with noisy object locations and to transform them toward the true locations. When applied as a preprocessing step on noisy annotations, our method greatly improves the performance of existing object detectors. Our method is applicable in scenarios where the images are only annotated with points roughly indicating object locations, instead of entire bounding boxes providing precise information on the object locations and extents. We test our method on three datasets and achieve a substantial improvement (e.g., 29.6% mAP on the COWC dataset) over existing methods for noise-robust object detection.