KOMPSAT Optical Image Registration via Deep-Learning Based OffsetNet Model

Jin Woo Yu, Che Won Park, Hyung Sup Jung

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


With the increase in satellite time series data, the utility of remote sensing data is growing. In the analysis of time series data, the relative positional accuracy between images has a significant impact on the results, making image registration essential for correction. In recent years, research on image registration has been increasing by applying deep learning, which outperforms existing image registration algorithms. To train deep learning-based registration models, a large number of image pairs are required. Additionally, creating a correlation map between the data of existing deep learning models and applying additional computations to extract registration points is inefficient. To overcome these drawbacks, this study developed a data augmentation technique for training image registration models and applied it to OffsetNet, a registration model that predicts the offset amount itself, to perform image registration for KOMSAT-2, -3, and -3A. The results of the model training showed that OffsetNet accurately predicted the offset amount for the test data, enabling effective registration of the master and slave images.

Original languageEnglish
Pages (from-to)1707-1720
Number of pages14
JournalKorean Journal of Remote Sensing
Issue number6-3
StatePublished - 2023


  • Data augmentation
  • Deep learning
  • Image registration
  • Optic image


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