用3D CNN实时检测卫星图像中的沙尘，精度超90%。
Near Real-Time Dust Aerosol Detection with 3D Convolutional Neural Networks on MODIS Data
- 用36个波段的3D卷积网络学习空间-光谱联合特征。
- 17个独立场景准确率达0.92，均方误差仅0.014。
- 支持全图快速处理，适合全球沙尘实时预警。
沙尘暴危害健康并降低能见度，需通过卫星实现快速检测。本文提出一种近实时系统，利用美国宇航局Terra和Aqua卫星（MODIS）的多波段图像，在像素级别标记沙尘。采用3D卷积神经网络学习全部36个波段及拆分热红外波段的模式，以区分沙尘、云层与地表特征。通过简单归一化和局部填充处理缺失数据。改进版本将训练速度提升21倍，并支持整幅图像的快速处理。在17个独立的MODIS场景中，模型准确率约0.92，均方误差为0.014。制图结果在沙尘羽流核心区域高度一致，多数误检发生在边缘区域。结果表明，联合波段与空间学习可实现全球尺度的及时沙尘预警；采用更宽输入窗口或注意力机制或可进一步优化边缘识别。
原文摘要 · Abstract (English)
Dust storms harm health and reduce visibility; quick detection from satellites is needed. We present a near real-time system that flags dust at the pixel level using multi-band images from NASA's Terra and Aqua (MODIS). A 3D convolutional network learns patterns across all 36 bands, plus split thermal bands, to separate dust from clouds and surface features. Simple normalization and local filling handle missing data. An improved version raises training speed by 21x and supports fast processing of full scenes. On 17 independent MODIS scenes, the model reaches about 0.92 accuracy with a mean squared error of 0.014. Maps show strong agreement in plume cores, with most misses along edges. These results show that joint band-and-space learning can provide timely dust alerts at global scale; using wider input windows or attention-based models may further sharpen edges.
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