Cristopher Castro Traba, Tom Hendrix, Amaury Perrocheau, Francesco Pasanisi, Finian Rogers, Aubrey Dunne, David Rijlaarsdam, Roberto Del Prete 等 10 位作者
原文摘要
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.
Sebastian Fieldhouse, Roberto Del Prete, Gabriele Daga, Nathaniel Rensly, Gabriele Meoni, Kea-Tiong Tang
原文摘要
Conventional focusing methods for Synthetic Aperture Radar (SAR) employ block-wise processing efficiently but remain latency-heavy processes that prevent the realisation of a closed-loop cognitive SAR system. We present an online SAR image-formation framework, StreamAZ, that treats sensing as a stream and produces focused images in a line by line fashion. Our method uses a tiny state-space surrogate model trained with teacher-student distillation and multi-stage losses. We evaluate the method on real SAR data from Maya4, a Sentinel-1-derived dataset containing raw, range-compressed, range-cell-migration-corrected, and azimuth-compressed products. Relative to a linewise digital-signal-processing baseline, our framework delivers approximately 70$\times$ lower latency and 130$\times$ lower memory use; on a single AMD CPU core it processes one row in 16 ms with a memory footprint of 6 MB whilst maintaining a focusing quality high enough to support downstream decisions, which we illustrate with vessel detection and flood-mapping tasks.