Problem
Research questions and friction points this paper is trying to address.
Faint Object Detection
Space Situational Awareness
Low SNR
Cislunar Environment
Optical Observations
Improving Faint Object Detection for Space Situational Awareness with Variational Autoencoders
🤖 AI Summary
该研究使用轻量分割网络和变分自编码器结合的方法，解决了光学空间态势感知中微弱移动物体检测的问题，通过自动去星和背景重建提高检测性能。
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Variational Autoencoder
Star Removal
Background Reconstruction
Space Situational Awareness
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