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Homography Augmented Momentum Contrastive Learning for SAR Image Retrieval

In: Synthetic Aperture Radar (SAR) Data Applications

Author

Listed:
  • Seonho Park

    (University of Florida)

  • Maciej Rysz

    (Miami University)

  • Kathleen M. Dipple

    (Air Force Research Laboratory (AFRL/RWWI), Eglin Air Force Base)

  • Panos M. Pardalos

    (University of Florida)

Abstract

Deep learning-based image retrieval has been a strongly emphasized area in computer vision. Representation embedding extracted by deep neural networks (DNNs) not only aims at containing semantic information of the image but also can manage large-scale image retrieval tasks scalably. In this chapter, we propose a deep learning-based image retrieval approach using homography transformation augmented contrastive learning to perform large-scale synthetic aperture radar (SAR) image search tasks. Moreover, a training method for the DNNs induced by contrastive learning that does not require any labeling procedure is introduced. This can facilitate the tractability of large-scale datasets with relative ease. Finally, we demonstrate the performance of the proposed method by conducting experiments on the polarimetric SAR image datasets.

Suggested Citation

  • Seonho Park & Maciej Rysz & Kathleen M. Dipple & Panos M. Pardalos, 2022. "Homography Augmented Momentum Contrastive Learning for SAR Image Retrieval," Springer Optimization and Its Applications, in: Maciej Rysz & Arsenios Tsokas & Kathleen M. Dipple & Kaitlin L. Fair & Panos M. Pardalos (ed.), Synthetic Aperture Radar (SAR) Data Applications, pages 63-78, Springer.
    • Handle: RePEc:spr:spochp:978-3-031-21225-3_3
    DOI: 10.1007/978-3-031-21225-3_3
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