卫星原位处理遥感图像，实现灾后建筑损毁快速评估
Optimizing Latent Representations for Robust Building Damage Assessment Onboard Earth Observation Satellites
- 将灾前图像转为紧凑潜在表示传至卫星，与灾后图像比对
- 在轨处理下仍保持高鲁棒性，强压缩时性能下降极小
- 适合应急响应、灾害监测等需快速决策的场景
自然灾害或战区发生后，快速识别受损建筑对应急响应至关重要。地球观测星座虽能提供及时的大范围覆盖，但数据回传限制、地面处理及人工判读常导致信息延迟。本工作提出一种基于AI的系统，可直接在卫星上完成建筑损毁评估（定位与分类），利用灾前和灾后高分辨率光学影像进行对比分析。灾前图像在地面编码为紧凑潜在表示并传输至卫星，与灾后新获取图像在轨比对。借助卫星端提升的计算能力与AI解析能力，实现数据源头处理，大幅减少需回传的数据量，同时保留任务相关内容，显著提升系统响应速度。通过系统性基准测试，评估了孪生网络、交叉注意力、潜在空间压缩及面向鲁棒性的数据增强等设计因素的影响。在xBD数据集上的实验表明，该方法在存在配准偏差情况下仍具可靠性和鲁棒性，即使在强压缩条件下性能损失极小。
原文摘要 · Abstract (English)
Rapid identification of damaged buildings after natural disasters or on war areas is crucial to support emergency response and prioritize interventions. Earth Observation constellations provide timely, large-scale coverage, but actionable information is often delayed by data downlink constraints, on-ground processing, and human interpretation. Reducing this latency is essential to improve decision-making responsiveness. In this work, we propose an original AI-based system that enables object-level building damage assessment (localization and damage classification) directly onboard satellites from pre-disaster and post-disaster highresolution optical imagery. Available pre-disaster images are encoded on ground into compact latent representations, transmitted to the satellite, and compared on-board with newly acquired post-event observations. Leveraging AI interpretation capabilities and increasing processing capabilities on-board satellites, the proposed design enables processing directly at the data source, reducing the amount of information to be downlinked while preserving task-relevant content and improving overall system responsivity. We explore the design space through a systematic benchmark of onboard-compatible variants, analyzing the impact of siamese processing, cross-attention, latent-space compression, and robustness-oriented data augmentation. Experiments on xBD dataset demonstrate reliable and robust damage assessment under misalignment, with minimal performance degradation under strong compression.
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