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Deep learning-based image retrieval has been emphasized in computer vision.Representation embedding extracted by deep neural networks (DNNs) not only aimsat containing semantic information of the image, but also can managelarge-scale image retrieval tasks. In this work, we propose a deeplearning-based image retrieval approach using homography transformationaugmented contrastive learning to perform large-scale synthetic aperture radar(SAR) image search tasks. Moreover, we propose a training method for the DNNsinduced by contrastive learning that does not require any labeling procedure.This may enable tractability of large-scale datasets with relative ease.Finally, we verify the performance of the proposed method by conductingexperiments on the polarimetric SAR image datasets.

Homography augumented momentum constrastive learning for SAR image retrieval