VDAM: VAE based domain adaptation for cloud property retrieval from multi-satellite data
VDAM: VAE based domain adaptation for cloud property retrieval from multi-satellite data
复制标题
DOI：
10.1145/3557915.3561044
复制
发表时间：
2022-11
期刊：
影响因子：
--
通讯作者：
Xin Huang;Chenxi Wang;S. Purushotham;Jianwu Wang
中科院分区：
文献类型：
--
作者：
Xin Huang;Chenxi Wang;S. Purushotham;Jianwu Wang
基于深度神经网络的领域自适应技术主要用于解决同质领域中的分布漂移问题，这些领域中的数据通常共享相似的特征空间并且具有相同的维度。然而，现实世界的应用程序经常处理来自完全不同的具有不同维度的特征空间的异构域。在我们的遥感应用中，一个主动传感器和一个被动传感器采集的两个遥感数据集是不同的。特别是，CALIOP积极测量每个大气层柱。在这项研究中，使用了25个对云相敏感的测量变量/特征，并对它们进行了完整的标记。VIIRS是一种成像辐射计，它收集地表和大气在可见光和红外波段的辐射测量结果。最近的研究表明，在复杂的大气(如重叠的云层和气溶胶层、冰雪表面的云等)中，被动传感器可能难以预测云/气溶胶类型。为了克服被动传感器云特性提取的难题，提出了一种新的基于VAE的方法来学习从多个卫星遥感数据(VDAM)中获取空间模式的域不变表示，以构建一种域不变的云特性提取方法来准确地分类被动传感数据集中的不同云类型(标签)。我们进一步利用标签空间上的基于权重的对齐方法，学习了一种适用于遥感应用的强大的域自适应技术。实验结果表明，在被动卫星数据集上，该方法的性能优于其他机器学习方法，并达到了较高的云属性提取精度。
Domain adaptation techniques using deep neural networks have been mainly used to solve the distribution shift problem in homogeneous domains where data usually share similar feature spaces and have the same dimensionalities. Nevertheless, real world applications often deal with heterogeneous domains that come from completely different feature spaces with different dimensionalities. In our remote sensing application, two remote sensing datasets collected by an active sensor and a passive one are heterogeneous. In particular, CALIOP actively measures each atmospheric column. In this study, 25 measured variables/features that are sensitive to cloud phase are used and they are fully labeled. VIIRS is an imaging radiometer, which collects radiometric measurements of the surface and atmosphere in the visible and infrared bands. Recent studies have shown that passive sensors may have difficulties in prediction cloud/aerosol types in complicated atmospheres (e.g., overlapping cloud and aerosol layers, cloud over snow/ice surface, etc.). To overcome the challenge of the cloud property retrieval in passive sensor, we develop a novel VAE based approach to learn domain invariant representation that capture the spatial pattern from multiple satellite remote sensing data (VDAM), to build a domain invariant cloud property retrieval method to accurately classify different cloud types (labels) in the passive sensing dataset. We further exploit the weight based alignment method on the label space to learn a powerful domain adaptation technique that is pertinent to the remote sensing application. Experiments demonstrate our method outperforms other state-of-the-art machine learning methods and achieves higher accuracy in cloud property retrieval in the passive satellite dataset.