Deep learning improves faint space object detection by removing stars
Improving Faint Object Detection for Space Situational Awareness with Variational Autoencoders
Computer Vision and Pattern RecognitionArtificial IntelligenceMachine Learning
Summary
Detecting very faint moving objects in space images is hard because bright stars and background light make them hard to see. The authors created a deep-learning method that first identifies and masks stars in telescope images, then uses a special autoencoder to fill in the missing areas realistically. This process cleans up the background, making it easier to spot faint moving objects like satellites or debris. When tested on real telescope data, the method improved the detection of these faint objects without erasing them.
What this means in practice
- •For satellite operators: Enhance detection of faint satellites and debris in cluttered optical images to improve space traffic management safety.
- •For astronomical observatories: Automate preprocessing of images to better isolate moving objects from dense star fields and background noise during observations.
Authors
Angela Cratere, Luca Ghilardi, Vishnu Reddy, Francesco Dell'Olio, Charalampos S. Kouzinopoulos, Roberto Furfaro
Abstract
We present a deep-learning pipeline for enhancing the detection of faint moving objects in optical space situational awareness (SSA) imagery through automated star removal and background reconstruction. Detecting low signal-to-noise ratio (SNR) objects remains extremely challenging in optical observations, particularly in the cislunar (X-GEO) environment, where structured sky backgrounds, dense stellar fields, and scattered moonlight significantly degrade the performance of classical detection algorithms. To address this problem, the proposed pipeline combines a lightweight segmentation network (Tiny-U-Net) to generate stellar masks with a partial-convolution variational autoencoder (astro-VAE), designed to learn the statistical distribution of astronomical backgrounds and perform context-aware inpainting of masked regions. The reconstructed background maps can then be used as a preprocessing step to suppress fixed sources and background inhomogeneities prior to detection. As a proof of concept, the approach is integrated with a shift-and-stack scheme and evaluated on real ground-based telescope observations targeting the X-GEO region. Results demonstrate that the method reconstructs star-free backgrounds with high fidelity, while preserving moving targets and significantly enhancing detectability, thereby providing an effective data-driven preprocessing strategy for faint moving-object detection in optical SSA scenarios.