轻量CNN实现卫星在轨实时识别海上船舶与风车
Lightweight CNNs for Embedded SAR Ship Target Detection and Classification
- 直接处理未聚焦SAR数据，省去复杂成像步骤
- 模型可在FPGA上运行，满足卫星算力限制
- 首次验证在轨分类船舶与风车的可行性
合成孔径雷达（SAR）数据可实现海面船只的大范围监视。然而，近实时监测受限于需下传全部原始数据、地面成像聚焦及后续分析。在轨处理生成高层产品可减少下传数据量，缓解带宽压力并降低延迟。但传统成像与处理算法受卫星内存、算力和计算资源限制。本文提出并评估了针对哨兵-1卫星条带模式和干涉宽幅模式获取的未聚焦SAR数据的轻量级神经网络，支持实时推理。结果表明，其中一模型具备在轨处理与FPGA部署的可行性。此外，通过二分类任务区分船舶与风车，验证了目标分类的可行性。
原文摘要 · Abstract (English)
Synthetic Aperture Radar (SAR) data enables large-scale surveillance of maritime vessels. However, near-real-time monitoring is currently constrained by the need to downlink all raw data, perform image focusing, and subsequently analyze it on the ground. On-board processing to generate higher-level products could reduce the data volume that needs to be downlinked, alleviating bandwidth constraints and minimizing latency. However, traditional image focusing and processing algorithms face challenges due to the satellite's limited memory, processing power, and computational resources. This work proposes and evaluates neural networks designed for real-time inference on unfocused SAR data acquired in Stripmap and Interferometric Wide (IW) modes captured with Sentinel-1. Our results demonstrate the feasibility of using one of our models for on-board processing and deployment on an FPGA. Additionally, by investigating a binary classification task between ships and windmills, we demonstrate that target classification is possible.
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