Homography augumented momentum constrastive learning for SAR image retrieval
This work addresses large-scale synthetic aperture radar image search for remote sensing applications, but it is incremental as it builds on existing contrastive learning techniques.
The paper tackled SAR image retrieval by proposing a homography-augmented contrastive learning method that eliminates labeling requirements, achieving improved performance on polarimetric SAR datasets with concrete experimental results.
Deep learning-based image retrieval has been emphasized 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. In this work, 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, we propose a training method for the DNNs induced 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 conducting experiments on the polarimetric SAR image datasets.