用于高光谱成像星上推理的轻量级云掩膜模型
Lightweight Cloud Masking Models for On-Board Inference in Hyperspectral Imaging
AI总结：
本研究评估了多种机器学习方法用于高光谱卫星成像的云掩膜，发现采用特征降维的CNN模型仅含597个参数且准确率超93%，在部署可行性与计算效率上表现最佳，支持星载AI系统开发。
摘要：
云和云阴影掩膜是高光谱卫星成像中关键的预处理步骤，能够提取高质量、可直接分析的数据。本研究评估了多种机器学习方法，包括XGBoost和LightGBM等梯度提升方法以及卷积神经网络（CNN）。所有提升方法和CNN模型的准确率均超过93%。在所研究的模型中，采用特征降维的CNN效率最高，在CPU和GPU上均兼顾了高准确率、低存储需求和快速推理时间。该版本的变体仅有多达597个可训练参数，在部署可行性、准确率和计算效率方面展现出最佳折衷。这些结果证明了轻量级人工智能（AI）模型在实时高光谱图像处理中的潜力，支持面向空间应用的星载卫星AI系统开发。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
