DRLO: Deep Representation Learning for Large Scale Off-track Satellite Remote Sensing Data
Conference
Huang, X, Wang, C, Zhang, W et al. (2023). DRLO: Deep Representation Learning for Large Scale Off-track Satellite Remote Sensing Data
. 1410-1418. 10.1109/BigData59044.2023.10386306
Huang, X, Wang, C, Zhang, W et al. (2023). DRLO: Deep Representation Learning for Large Scale Off-track Satellite Remote Sensing Data
. 1410-1418. 10.1109/BigData59044.2023.10386306
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.
