Self-supervised learning models can effectively adapt to the prevailing big data trend, leading to their extensive applications in various domains. Nevertheless, image degradation caused by speckle noise leads to a performance decline of self-supervised learning models in Synthetic Aperture Radar Automatic Target Recognition (SAR ATR). In this article, we introduce a Noise Reduction Masked Autoencoder (NR-MAE) method that integrates the Masked Image Modeling(MIM) with filtering algorithms. The NR-MAE reduces speckle noise effects through filtering algorithms, enabling the model to learn target features more accurately and efficiently. Experiment indicates that NR-MAE outperforms the MAE method in SAR image recognition accuracy, showing an improvement of 8% to 10% in performance under the 1/32scale small sample training set. Furthermore, an ablation study on the filtering algorithm's hyperparameters affirms the model's robustness to changes in these parameters.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.