arXiv:2611.14857 · v1

In-orbit demonstration of object-based change detection applied to the maritime domain

首次提交 2026-11-11 08:13

版本更新 2026-11-11 08:13

雷达观测 2026-11-13 10:09

摘要

This article presents the successful in-orbit validation of an object-based change detection pipeline for low latency maritime monitoring onboard the CogniSAT-6 mission. We propose and validate a lightweight, three-phase operational sequence: optimized ground-based dual-acquisition scheduling, real-time onboard AI-based vessel segmentation, and multi-temporal matching. Upon acquiring consecutive images over a targeted Area of Interest (AoI), the onboard system correlates vessel detections using a hybrid matching approach that integrates the Hungarian algorithm for optimal assignment with RANSAC-driven spatial and size filters for outlier rejection. A series of in-orbit validations demonstrated robust performance across 11 diverse maritime scenarios, achieving valid recall and precision values (considering only vessel size and location) exceeding 0.8. The validation demonstrated the ability of the system to track both cooperative targets and uncooperative "dark" vessels strictly through geometric consistency. This capacity to autonomously generate and act upon real-time intelligence establishes a foundation for next-generation Insight-as-a-Service (IaaS) and tipping-and-cueing architectures.

作者备注：11 pages, 8 figures