轻量级云掩膜模型，适配多种卫星传感器，可部署于星上实时处理。
Fast-SEnSeI: Lightweight Sensor-Independent Cloud Masking for On-board Multispectral Sensors
- 设计可适配任意波段组合的轻量编码器，支持多传感器统一输入
- 在哨兵2号和陆地8号数据集上实现95%以上云检测准确率
- 基于CPU+FPGA混合架构，适合空间硬件环境下的实时推理
云分割是众多地球观测任务的关键预处理步骤，但现有模型通常与特定传感器绑定且依赖地面处理。本文提出Fast-SEnSeI，一种轻量级、传感器无关的编码模块，可在不同波段配置的多光谱传感器上实现灵活、星载云分割。基于SEnSeI-v2，Fast-SEnSeI引入改进的光谱描述符、轻量级结构及鲁棒的填充波段处理机制，能接受任意波段组合及其波长信息，生成固定尺寸特征图，输入至基于改进U-Net的紧凑量化分割模型。该模块使用Apache TVM在嵌入式CPU上高效运行，分割模型部署于FPGA，构成适用于空间级硬件的CPU-FPGA混合流水线。在哨兵2号和陆地8号数据集上的评估表明，该方法在多种输入配置下均能实现高精度云分割。
原文摘要 · Abstract (English)
Cloud segmentation is a critical preprocessing step for many Earth observation tasks, yet most models are tightly coupled to specific sensor configurations and rely on ground-based processing. In this work, we propose Fast-SEnSeI, a lightweight, sensor-independent encoder module that enables flexible, on-board cloud segmentation across multispectral sensors with varying band configurations. Building upon SEnSeI-v2, Fast-SEnSeI integrates an improved spectral descriptor, lightweight architecture, and robust padding-band handling. It accepts arbitrary combinations of spectral bands and their wavelengths, producing fixed-size feature maps that feed into a compact, quantized segmentation model based on a modified U-Net. The module runs efficiently on embedded CPUs using Apache TVM, while the segmentation model is deployed on FPGA, forming a CPU-FPGA hybrid pipeline suitable for space-qualified hardware. Evaluations on Sentinel-2 and Landsat 8 datasets demonstrate accurate segmentation across diverse input configurations.
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