Onboard Terrain Classification via Stacked Intelligent Metasurface-Diffractive Deep Neural Networks from SAR Level-0 Raw Data
This addresses the problem of reducing reliance on expensive downlink bandwidth and high-power computing for remote sensing applications, though it appears incremental as it builds on existing diffractive deep neural network concepts.
The paper tackles real-time onboard terrain classification from Sentinel-1 level-0 raw IQ data by using a Stacked Intelligent Metasurface to perform inference in the analog wave domain, achieving performance levels around 90% in accuracy, precision, recall, and F1 Score.
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.