用智能超表面直接在雷达原始数据上实时分类地形，无需传回海量数据。
Onboard Terrain Classification via Stacked Intelligent Metasurface-Diffractive Deep Neural Networks from SAR Level-0 Raw Data
- 通过多层衍射神经网络，在电磁波传播中完成特征提取。
- 直接从原始雷达数据实现约90%的分类准确率，涵盖精确率、召回率等指标。
- 适合需要低带宽、低功耗星载处理的遥感任务，如应急监测。
本文提出一种新型方法，可直接利用哨兵-1（Sentinel-1, S1）Level-0原始I/Q数据，在轨实时完成地形分类，借助堆叠式智能超表面（Stacked Intelligent Metasurface, SIM）在模拟波域中执行推理。与传统数字深度神经网络不同，所提出的多层衍射深度神经网络（D$^2$NN）通过电磁波在多层超表面间的传播，实现自动特征提取。该设计不仅显著降低对地面站高带宽传输和高功耗计算的依赖，且在真实原始I/Q数据上实现了约90%的分类准确率，涵盖精确率、召回率与F1分数。本方法有助于弥合下一代遥感任务与星载处理需求之间的差距，为计算高效遥感应用铺平道路。
原文摘要 · Abstract (English)
This paper introduces a novel approach for real-time onboard terrain classification from Sentinel-1 (S1) level-0 raw In-phase/Quadrature (IQ) data, leveraging a Stacked Intelligent Metasurface (SIM) to perform inference directly in the analog wave domain. Unlike conventional digital deep neural networks, the proposed multi-layer Diffractive Deep Neural Network (D$^2$NN) setup implements automatic feature extraction as electromagnetic waves propagate through stacked metasurface layers. This design not only reduces reliance on expensive downlink bandwidth and high-power computing at terrestrial stations but also achieves performance levels around 90\% directly from the real raw IQ data, in terms of accuracy, precision, recall, and F1 Score. Our method therefore helps bridge the gap between next-generation remote sensing tasks and in-orbit processing needs, paving the way for computationally efficient remote sensing applications.
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