轻量级模型实现高精度云掩膜，适合卫星实时处理
Lightweight Cloud Masking Models for On-Board Inference in Hyperspectral Imaging
- 用轻量CNN与梯度提升方法做云和云影检测
- 模型准确率超93%，参数少至597个仍保持高效
- 适合部署在卫星上，兼顾速度、存储与精度
云和云影掩膜是高光谱卫星成像中至关重要的预处理步骤，可提取高质量、可分析的数据。本研究评估了多种机器学习方法，包括XGBoost、LightGBM等梯度提升算法及卷积神经网络（CNN）。所有提升方法和CNN模型的准确率均超过93%。其中，经过特征压缩的CNN表现最优，兼具高准确率、低存储需求与快速推理能力，可在CPU和GPU上运行。参数量仅达597个的变体版本，在部署可行性、准确率与计算效率间取得最佳平衡。结果表明，轻量级人工智能模型有望实现实时高光谱图像处理，支持空间应用中星载AI系统的发展。
原文摘要 · Abstract (English)
Cloud and cloud shadow masking is a crucial preprocessing step in hyperspectral satellite imaging, enabling the extraction of high-quality, analysis-ready data. This study evaluates various machine learning approaches, including gradient boosting methods such as XGBoost and LightGBM as well as convolutional neural networks (CNNs). All boosting and CNN models achieved accuracies exceeding 93%. Among the investigated models, the CNN with feature reduction emerged as the most efficient, offering a balance of high accuracy, low storage requirements, and rapid inference times on both CPUs and GPUs. Variations of this version, with only up to 597 trainable parameters, demonstrated the best trade-off in terms of deployment feasibility, accuracy, and computational efficiency. These results demonstrate the potential of lightweight artificial intelligence (AI) models for real-time hyperspectral image processing, supporting the development of on-board satellite AI systems for space-based applications.
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