As part of a collaborative research project between CVLab and LASTRO, we are exploring novel techniques for determining the rotational and physical properties of space debris. For this purpose, we are currently developing methods for the detection and extraction of space debris observations from large astronomical data archives. These archives contain observational data over a 10-year period and include a large amount of random satellite and space debris observations. On the astronomical images, these objects appear as characteristic streaks, most of which cross the entire detector during the several minutes of exposure time.
In order to monitor the performance of our streak detection methods, we incorporate synthetic streaks to the data before processing. This allows us to determine the detection efficiency and to verify orbit determination routines. Currently, these synthetic streaks are randomly generated features that do not reflect any information on orbit, size or shape. Improving the generation of synthetic streaks incorporating this information would make them appear more realistic and would improve our algorithm’s robustness in the analysis of real streaks.
The goal of our analysis is to obtain as much information as possible from the observed space objects. An important property that we want to determine is the rotation rate axis. This tumbling state can theoretically be obtained from the intensity profile of the observed streak. However, for certain objects or tumbling states, we might not have enough data for a robust analysis. A priori knowledge on the size and shape of the object can be used to generate synthetic data that can be used to constrain the tumbling state of the observed objects.
Project Scope
The goal of this project is to develop a tool that allows the insertion of realistic synthetic observations of space objects into astronomical images. These synthetic observations should be based on an artificial population of space objects that resembles the real population as closely as possible. This population is then used to implant synthetic streaks into real data. While the observatory location, telescope and instrument determine which objects are visible at the time of observations, object shape, observing geometry (illumination conditions), rotation, atmospheric extinction and seeing define the precise appearance of the synthetic streak.The final outcome of this project will be the synthetic population of space objects and a tool that inserts the synthetic streaks into real data.
Tasks
- Familiarization with astronomical data archives
(data products, instruments, sensors, environment that influences the appearance of space objects on astronomical images) - Implementation of a rendering engine (Blender)
- Generation of a synthetic population of space objects, incorporating orbits, shapes, sizes and rotations
- Development of a tool to implant synthetic streaks on astronomical images
Prerequisites:
- Open to coding in Python
- Interest in image rendering
- Familiarity with rendering (Blender) and camera basics (I.E. pinhole camera model) is a plus.
- Interest in computer vision or machine learning is a plus.
Supervisor:
LASTRO/CVLab/eSpace (Prof. Jean-Paul Kneib/Stephan Hellmich/Andrew Price)
Type of Project: Master project (can be adapted for TP-IV)
Duration: 14 weeks (Official start/end date: September 19-December 22)
Submission of final report: January 15
Final Presentation: TBD