Deep Neural Network closed-loop with raw data for optical resident space object detection

Abstract

Abstract Optical survey is an important means for observing resident space object and space situational awareness. With the application of astronomical technique and reduction method, wide field of view telescopes have made significant contributions in discovering and identifying resident space objects. However, as the development of modern optical and electronic technology, the detection limit of instrument and infrastructure has been greatly extended, leading to an extensive number of raw images and much more sources in these images. Challenges arise when reducing this data in traditional measurement and calibration ways. Based on the amount of data, it is particularly feasible and reliable to apply machine learning algorithms. Here an end-to-end deep learning framework is developed, it is trained with apriori information on raw detections and automatic detection task is performed on new data acquired. The closed-loop is evaluated based on consecutive CCD images obtained with a dedicated space debris survey telescope, it is demonstrated that our framework can achieve high performance compared with traditional method, and with data fusion the efficiency of system can be improved without changing hardware or deploying new devices. The technique deserves a wider application in many fields of observational astronomy.