DRLO: Deep Representation Learning for Large Scale Off-track Satellite Remote Sensing Data
DRLO: Deep Representation Learning for Large Scale Off-track Satellite Remote Sensing Data
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DOI：
10.1109/bigdata59044.2023.10386306
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发表时间：
2023-12
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影响因子：
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通讯作者：
Xin Huang;Chenxi Wang;Wenbin Zhang;Sanjay Purushotham;Jianwu Wang
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作者：
Xin Huang;Chenxi Wang;Wenbin Zhang;Sanjay Purushotham;Jianwu Wang
有源和无源卫星传感器测量值的搭配是指将来自几乎同时观察同一地理区域但具有不同空间分辨率和视角的两个传感器的数据组合起来。这种并置数据通常称为在轨数据，带有来自主动传感器的精确产品标签，但仅包含直接位于主动卫星轨道路径上的像素。因此，其空间覆盖范围非常有限，特别是与大量偏离轨道的数据相比。处理丰富且信息密集的轨道外数据对于训练机器学习模型至关重要，该模型可以有效地将这些数据的独特特征与轨道上数据相结合。然而，大量偏离轨道的数据给这些模型带来了巨大的挑战。为了解决遥感应用中大量未标记偏离轨道数据的挑战，我们引入了一种具有 VAE 和域适应方法的自监督表示学习模型，以学习在轨和偏离轨道数据的域不变分类器。通过使用偏离轨道数据的 VAE 生成模型对偏离轨道数据进行预训练，模型的性能得到了增强，以学习可以转移到下游域适应和分类任务的良好表示。分类器建立在这些表示的基础上，对被动传感数据中的不同云类型进行分类，目标是在云属性检索中实现更高的准确性。广泛的定量和定性评估表明，我们的方法在偏离轨道遥感数据的云属性检索方面实现了更高的准确性。
Collocation of measurements from active and passive satellite sensors refers to the combination of data from two sensors that observe the same geographic area at nearly the same time but with differing spatial resolutions and viewing angles. This collocated data, often known as on-track data, comes with precise product labels from the active sensor but comprises only the pixels located directly on the path of an active satellite’s orbit. As a result, its spatial coverage is quite limited, especially when compared to the vast quantities of off-track data. Handling the abundant and information-dense off-track data is crucial for training machine learning models that can effectively integrate the unique features of this data along with on-track data. However, the sheer volume of off-track data presents significant challenges for these models. To address the challenges of large amounts of unlabeled off-track data in remote sensing applications, we introduce a self-supervised representation learning model with VAE and domain adaptation methods to learn a domain invariant classifier for the on-track and off-track data. The model’s performance is enhanced by pre-training off-track data with VAE generative model using off-track data, to learn a good representation that can be transferred to the down-streaming domain adaptation and classification tasks. The classifier is built on these representations to classify different cloud types in passive sensing data, with the goal of achieving higher accuracy in cloud property retrieval. Extensive quantitative and qualitative evaluation demonstrate our method achieves higher accuracy in cloud property retrieval for off-track remote sensing data.