High-Level Project Summary
NEOSSat provides images that are taken and stored in a database for analysis. Our project proposes an analytical method to process images to detect object movement. The benefits of this method are a direct way to detect an object that moves, and a way to greatly speed up the imaging processing speed (only on intermediate images that have sparse data sets).
Link to Project "Demo"
Link to Final Project
Detailed Project Description
In space, there are many objects to detect and analyze, including stars, asteroids, space debris, comets, etc. NEOSSat provides images that are taken and stored in a database for analysis. The typical analysis steps include processing the desired images to search for objects, recording objects' coordinates, and then repeating the process for images taken after a given time interval. By comparing the objects' coordinates, one can deduct the object's moving trajectories, after processing all objects in all images.
Our project proposes a few steps to greatly speed up this process. The main idea is to use the initial few images of the same camera (given desired time frame) to establish a "common objects" list. Instead of looking for objects that move, this step identifies objects that have identical coordinates. These unmoved objects then establish as a "noise filter". Starting from the images taken after this initial time period, all images are to 'subtract' this 'noise image' to generate the intermediate data for further analysis,
These intermediate image files then conceptually ONLY have objects that move, i.e. a very sparse data set compared with the original image will all objects. Next follows the typical outlined steps should be able to find out any object's moving trajectory equation.
There are two key parameters in this: 1) the movement tolerance for an object to determine if an object is stationary,
2) the time interval of images for generating the 'noise filter' image. Adjusting these two parameters allows us to throttle the size of the sparse data sets and the sensitivity of the object movement detection. For example, this methodology can be applied to the NEOSSat database on a per-day basis with initial images for the first 5 minutes of each hour (thus 24 noise filters, one per hour). Therefore, typical stars and galaxies will be in the noise filter set, and leave comets, asteroids, debris, satellites, etc., that move in the sparse data set/intermediate image that can be stored as an additional file to the original image.
The benefits of this method are a direct way to detect an object that moves, and a way to greatly speed up the imaging processing speed (only on intermediate images that have sparse data sets). In addition, this can also be used to detect asteroids and cosmic bodies heading towards Earth, as well as an unusual trajectory or speed which can be used to investigate the possibility of unidentified flying objects (UFOs).
Space Agency Data
Hackathon Journey
This Space Apps is a great opportunity for me to explore Infinity, identify the questions and solve them. As a 16-year-old high school student, I also figured out that there are so many experts in STEM fields from all over the world. It's an exciting experience to share my ideas, take the challenge, and try to make our world better with people from all over the world.
References
Tags
#NEOSSat #spacedebris #movingobjects #asteroids #comets
Global Judging
This project has been submitted for consideration during the Judging process.