Homography Augmented Momentum Contrastive Learning for SAR Image Retrieval

Seonho Park (), Maciej Rysz (), Kathleen M. Dipple () and Panos M. Pardalos ()
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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

A chapter in Synthetic Aperture Radar (SAR) Data Applications, 2022, pp 63-78 from Springer

Abstract: 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.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:spr:spochp:978-3-031-21225-3_3

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DOI: 10.1007/978-3-031-21225-3_3

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