论文标题
云分类和无监督的深度学习
Cloud Classification with Unsupervised Deep Learning
论文作者
论文摘要
我们为云表征提出了一个利用现代无监督的深度学习技术的框架。尽管以前基于神经网络的云分类模型已经使用了监督的学习方法，但无监督的学习使我们避免将模型限制为基于历史云分类方案的人造类别，并可以发现新颖，更详细的分类。我们的框架直接从NASA的中等分辨率成像光谱仪（MODIS）卫星仪器产生的辐射数据中学习了云功能，从而从数百万张图像中得出了云特性，而无需在训练过程中依赖预定的云类型。我们提出了初步结果，表明我们的方法从辐射数据中提取了与物理相关的信息，并产生有意义的云类。
We present a framework for cloud characterization that leverages modern unsupervised deep learning technologies. While previous neural network-based cloud classification models have used supervised learning methods, unsupervised learning allows us to avoid restricting the model to artificial categories based on historical cloud classification schemes and enables the discovery of novel, more detailed classifications. Our framework learns cloud features directly from radiance data produced by NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) satellite instrument, deriving cloud characteristics from millions of images without relying on pre-defined cloud types during the training process. We present preliminary results showing that our method extracts physically relevant information from radiance data and produces meaningful cloud classes.
