堆叠智能超表面-衍射深度神经网络用于星上SAR Level-0原始数据地形分类
Stacked Intelligent Metasurface-Diffractive Deep Neural Networks for Onboard Terrain Classification from SAR Level-0 Raw Data
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中文总结 AI 辅助
本文提出堆叠智能超表面-衍射深度神经网络（SIM-D$^2$NN），利用物理波传播实现星上SAR原始数据直接地形分类，达到约90%性能，减少下行链路依赖。
中文摘要 AI 辅助
直接从Level-0原始合成孔径雷达（SAR）数据进行实时地形分类仍受传统数字中心范式的限制，其中处理噪声高维补丁受计算密集型处理器和显著下行链路延迟的阻碍。为解决这些根本性限制，本工作为自主星上感知建立了新的研究范式，提出堆叠智能超表面-衍射深度神经网络（SIM-D$^2$NN）。该架构利用物理波传播介质将推理任务从数字处理器卸载到物理设备。通过执行波内特征映射，SIM-D$^2$NN推动向集成“边传输边计算”框架的转变，为传统“先数字化后处理”序列提供替代方案。多层超表面位于卫星通信模块的前端。初始层通过幅度和相位调整调制原始SAR数据，其中引入90$^\circ$相位旋转作为轻量但有效的增强策略，以增强对噪声和多普勒失真的鲁棒性。后续层学习可变相移，实现用于分类任务的高级特征映射。地面站的分类结果可直接基于每个天线接收的信号幅度获得。该设计减少了对下行链路带宽和高功率地面计算的依赖，在真实原始SAR数据的二分类任务中，准确率、精确率、召回率和F1分数均达到约90%。因此，我们的方法有助于弥合下一代遥感任务与在轨处理需求之间的差距，为计算高效的遥感应用铺平道路。
英文摘要
Real-time terrain classification directly from Level-0 raw Synthetic Aperture Radar (SAR) data remains restricted by traditional digital-centric paradigms, where the processing of noisy, high-dimensional patches is hindered by computationally intensive processors and significant downlink latency. To address these fundamental limitations, this work establishes a new research paradigm for autonomous on-board sensing by proposing a Stacked Intelligent Metasurface-Diffractive Deep Neural Network (SIM-D$^2$NN). This architecture leverages the physical wave-propagation medium to offload inference tasks from digital processors to a physical device. By executing in-wave feature mapping, the SIM-D$^2$NN facilitates a move toward an integrated `compute-while-transmitting' framework, providing an alternative to the traditional `digitize-then-process' sequence. The multi-layer metasurface is positioned at the forefront of the satellite communication module. The initial layer modulates the raw SAR data through both amplitude and phase adjustments, where a 90$^\circ$ phase rotation is introduced as a lightweight but effective augmentation strategy to enhance robustness against noise and Doppler distortions. Subsequent layers learn variable phase shifts, enabling advanced feature mapping for the classification task. The classification results at the terrestrial station can be directly obtained based on the signal amplitude received at each antenna. This design reduces reliance on downlink bandwidth and high-power terrestrial computing, achieving performance around 90% in the binary task directly from real raw SAR data in terms of accuracy, precision, recall, and F1 Score. Therefore, our method helps bridge the gap between next-generation remote sensing tasks and in-orbit processing needs, paving the way for computationally efficient remote sensing applications.
发表机构
- School of Electrical and Electronics Engineering, Nanyang Technological University（南洋理工大学电气与电子工程学院）
机构由 AI 辅助整理，请以论文原文为准。
