Deep Multi-Sensor Domain Adaptation on Active and Passive Satellite Remote Sensing Data-猫眼课题宝

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2020

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Xin Huang;Sahara Ali;Sanjay Purushotham;Jianwu Wang;Chenxi Wang;Zhibo Zhang

研究表明，随机森林 (RF) 等机器学习 (ML) 算法在遥感应用中的性能可能优于基于物理的算法。然而，这些机器学习算法不太适合从异构源（例如多个有源和无源传感器）中学习。例如，RF 可以针对云气溶胶激光雷达和红外探路者卫星观测 (CALIPSO) 或可见红外成像辐射计套件 (VIIRS) 传感器数据进行开发，但它无法共同从这两个传感器中学习，因为传感器之间存在特征（变量）不匹配。另一方面，域适应技术已经被开发来处理来自多个源或域的数据。但大多数现有的域适应方法假设源域和目标域是同构的，即它们具有相同的特征空间，并且域之间的差异主要是由于数据分布漂移而产生的。然而，许多现实世界的应用程序经常处理来自完全不同特征空间的异构域的数据。例如，在我们的遥感应用中，源域即CALIPSO包含主动星载激光雷达传感器收集的25个属性的数据；目标域，即VI-IRS，包含被动光谱辐射计传感器收集的另一组20个属性的数据。 CALIPSO对气溶胶类型和云相具有较好的表征能力和敏感性，而VIIRS具有较宽的测绘带和较好的空间覆盖范围，但在区分不同垂直层面的大气物体方面存在先天的弱点。为了解决整个功能的不匹配问题

Studies have shown machine learning (ML) algorithms such as Random Forests (RF) could outperform the physical-based algorithms in remote sensing applications. However, these ML algorithms are not well-suited to learn from heterogeneous sources such as multiple active and passive sensors. For example, RF can be either developed for Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) or Visible Infrared Imaging Radiometer Suite (VIIRS) sensor data, but it cannot jointly learn from both these sensors since there is a mismatch of features (variables) among the sensors. On the other hand, domain adaptation techniques have been developed to handle data from multiple sources or domains. But most existing domain adaptation approaches assume that the source and target domains are homogeneous i.e., they have the same feature space, and the difference between domains primarily arises due to the data distribution drifting. Nevertheless, many real world applications often deal with data from heterogeneous domains that come from completely different feature spaces. For example, in our remote sensing application, the source domain, namely CALIPSO, contains data of 25 attributes collected by the active spaceborne Lidar sensor; and the target domain, namely VI-IRS, contains another group of data of 20 attributes collected by passive spectroradiometer sensor. CALIPSO has better representation capability and sensitivity to aerosol types and cloud phase, while VIIRS has wide swaths and better spatial coverage but has inherent weakness in differentiating atmospheric objects on different vertical levels. To address this mismatch of features across the

Deep Multi-Sensor Domain Adaptation on Active and Passive Satellite Remote Sensing Data

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