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.

翻译：我们提出了一个云特性框架,利用现代不受监督的深层学习技术。虽然以前以神经网络为基础的云分类模型使用了有监督的学习方法,但未经监督的学习使我们能够避免将模型局限于基于历史云分类办法的人工类别,并能够发现新的、更详细的分类。我们的框架直接从美国航天局的中分辨率成像分光仪(MODIS)卫星仪器产生的光亮数据中学习云特性,从数百万图像中产生云特性,而无需在培训过程中依赖预先确定的云型。我们提出初步结果,表明我们的方法从光亮数据中提取了与物理相关的信息,并产生了有意义的云类。