Advanced Algorithms for Segmentation of Space Debris Astronomical Images

Abstract

During astronomical observations, images of selected part of the sky are made by the Slovak 70cm telescope specialized on space debris tracking. Every pixel of this frame can be represented by three data: position on the horizontal $X$ axis, vertical $Y$ axis, respectively and the intensity value that can range from 0 to 65536. The intensity value in the order of thousands or higher indicates presence of an orbital or extraterrestrial object such as a star, planet, space debris, or even electromagnetic field interference, celestial plane background and other artefacts. In this paper, we present the methodology and proof of concept of our design for processing of astronomical images and a novel space debris tracklet building process using a machine learning method by exploiting Long Short Term Memory (LSTM) architectures. Machine learning models need a fair amount of data examples for training. However, there are not enough sequences captured by the telescope, therefore we train a neural network with synthetic artificial training data based on known sky observations. Information about moving objects in the Earth's orbit is visualized as sequences of positions in time.