嵌入式双时相建筑损毁评估用于星上数据缩减
Embedded Bi-Temporal Building Damage Assessment for On-Board Data Reduction
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中文总结 AI 辅助
针对灾后建筑损毁快速评估受限于卫星链路带宽的问题，提出基于YOLOX孪生检测器的双时相流程，通过压缩参考影像并引入潜在空间偏移校正，实现星上目标级数据缩减，并在嵌入式平台上验证了性能与鲁棒性。
中文摘要 AI 辅助
自然灾害后快速评估建筑损毁对于支持应急响应至关重要。地球观测卫星可在事件发生后不久获取相关影像，但数据利用受到上行链路和下行链路容量以及地面处理延迟的限制。我们通过一个基于YOLOX衍生的孪生检测器构建的双时相建筑损毁评估流程来解决这一问题，该流程旨在压缩地面/空间链路两端的信息。在地面，灾前参考影像被编码为紧凑的潜在空间——压缩倍数高达64倍——并上行传输至卫星。在星上，该参考与新的灾后采集影像进行比较，使得下行链路仅传输可操作的目标级产品、边界框和损毁类别，而非完整场景。这削减了两个方向交换的数据量，而在xBD数据集上，强压缩的参考仍保留了大部分检测性能。由于星上采集存在残余的灾前/灾后配准误差，我们引入了一个潜在空间偏移估计与校正模块，该模块从粗特征层级回归全局偏移，并在融合前重新对齐灾后特征。它显著提高了对失配准的鲁棒性——尤其是在大偏移下，此时仅融合的变体性能崩溃——同时提高了标称精度，并保持与最强压缩的兼容性。最后，我们将该流程移植到两个嵌入式目标平台：Xilinx Versal VCK190和NVIDIA Jetson AGX Orin，并报告硬件性能（延迟、吞吐量、功耗效率）。核心检测器及其压缩模块可干净地移植到两者，但实现远距离鲁棒性所需的算子仅能在Jetson GPU上运行，而Versal DPU则不支持。
英文摘要
Rapid assessment of building damage after natural disasters is essential to support emergency response. Earth Observation satellites can acquire relevant imagery shortly after an event, but exploitation is limited by uplink and downlink capacity and by ground-processing latency. We address this with a bi-temporal building damage assessment pipeline built on a siamese detector derived from YOLOX, designed to compress information at both ends of the ground/space link. On the ground, pre-disaster reference images are encoded into a compact latent space -- compressed by up to a factor of 64 -- and uplinked to the satellite. On board, this reference is compared with a fresh post-disaster acquisition so that the downlink carries only actionable object-level products, bounding boxes and damage classes, instead of full scenes. This cuts the data exchanged in both directions, while on xBD the strongly compressed reference still preserves most of the detection performance. Because on-board acquisitions suffer from residual pre/post co-registration errors, we introduce a latent-space shift estimation and correction module that regresses the global offset from the coarse feature level and realigns the post-disaster features before fusion. It substantially improves robustness to de-registration -- especially under large shifts, where fusion-only variants collapse -- while also raising nominal accuracy and remaining compatible with the strongest compression. We finally port the pipeline to two embedded targets, a Xilinx Versal VCK190 and an NVIDIA Jetson AGX Orin, and report hardware performance (latency, throughput, power efficiency). The core detector and its compression port cleanly to both, but the operators needed for long-range robustness survive only on the Jetson GPU, whereas the Versal DPU does not.
发表机构
- IRT Saint Exupéry（IRT圣埃克苏佩里研究所）
- CNES（法国国家空间研究中心）
机构由 AI 辅助整理，请以论文原文为准。
