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.
@article{arxiv.2509.20991,
title = {Fast-SEnSeI: Lightweight Sensor-Independent Cloud Masking for On-board Multispectral Sensors},
author = {Jan Kněžík and Jonáš Herec and Rado Pitoňák},
journal= {arXiv preprint arXiv:2509.20991},
year = {2025}
}
Comments
This is a preprint of a paper accepted for the EDHPC 2025 Conference