Improving Faint Object Detection for Space Situational Awareness with Variational Autoencoders — Paper

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.

Authors 6

Angela Cratere, Luca Ghilardi, Vishnu Reddy, Francesco Dell'Olio, Charalampos S. Kouzinopoulos, Roberto Furfaro

Specification

Official page: arxiv.org/abs/2609.11269
Source:arXiv (Atom API + RSS)T1observed 55 min agohigh
Arxiv announce type: new
Source:arXiv (Atom API + RSS)T1observed 54 min agohigh
arXiv id: 2609.11269
Source:arXiv (Atom API + RSS)T1observed 55 min agohigh
Categories: cs.CV, cs.AI, cs.LG
Source:arXiv (Atom API + RSS)T1observed 55 min agohigh
PDF: arxiv.org/pdf/2609.11269
Source:arXiv (Atom API + RSS)T1observed 55 min agohigh
Primary category: cs.CV
Source:arXiv (Atom API + RSS)T1observed 55 min agohigh
Published: 11 Sept 2026
Source:arXiv (Atom API + RSS)T1observed 55 min agohigh

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9

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T19

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54 min ago

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Authors: Angela Cratere, Luca Ghilardi, Vishnu Reddy

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Value	Valid from → to	Status	Source	Confidence	Extractor
11 Sept 2026	11 Sept 2026 21:02 UTC → current	current	arXiv (Atom API + RSS)T1	high	deterministic

Claims are temporal and append-only: a new observation closes the previous claim (valid_to) instead of overwriting it. Conflicting claims from different sources are kept side by side and flagged — never averaged. Methodology →

11 Sept 2026
Property changedPaperImproving Faint Object Detection for Space Situational Awareness with Variational Autoencoders
Improving Faint Object Detection for Space Situational Awareness with Variational Autoencoders: arxiv announce type changed from cross to new
Arxiv announce typecross→newarxiv
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11 Sept 2026
New paperPaperImproving Faint Object Detection for Space Situational Awareness with Variational Autoencoders
New paper: Improving Faint Object Detection for Space Situational Awareness with Variational Autoencoders
arxiv
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Source documents
Source	Document	Type	Tier	Last observed	Snapshots
arXiv (Atom API + RSS)	rss.arxiv.org/rss/cs.CV	feed	T1· Official	54 min ago	1
arXiv (Atom API + RSS)	rss.arxiv.org/rss/cs.LG	feed	T1· Official	55 min ago	1

Tier 1 = official/primary, 2 = quality secondary, 3 = community, 4 = unverified. Every snapshot is archived; see all sources and the methodology.