SCF-CIL: A Multi-Stage Regularization-Based SAR Class-Incremental Learning Method Fused with Electromagnetic Scattering Features
Synthetic aperture radar (SAR) recognition systems often need to collect new data and update the network accordingly. However, the network faces the challenge of catastrophic forgetting, where previously learned knowledge might be lost during the incremental learning of new data. To improve the appl...
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MDPI AG
2025-04-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/9/1586 |
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| author | Yunpeng Zhang Mengdao Xing Jinsong Zhang Sergio Vitale |
| author_facet | Yunpeng Zhang Mengdao Xing Jinsong Zhang Sergio Vitale |
| author_sort | Yunpeng Zhang |
| collection | DOAJ |
| description | Synthetic aperture radar (SAR) recognition systems often need to collect new data and update the network accordingly. However, the network faces the challenge of catastrophic forgetting, where previously learned knowledge might be lost during the incremental learning of new data. To improve the applicability and sustainability of SAR target classification methods, we propose a multi-stage regularization-based class-incremental learning (CIL) method for SAR targets, called SCF-CIL, which addresses catastrophic forgetting. This method offers three main contributions. First, for the feature extractor, we fuse the convolutional neural network features with the scattering center features using a cross-attention feature fusion structure, ensuring both the plasticity and stability of the extracted features. Next, an overfitting training strategy is applied to provide clustering space for unseen classes with an acceptable trade-off in the accuracy of the current classes. Finally, we analyze the influence of training with imbalanced data on the last fully connected layer and introduce a multi-stage regularization method by dividing the calculation of the fully connected layer into three parts and applying regularization to each. Our experiments on SAR datasets demonstrate the effectiveness of these improvements. |
| format | Article |
| id | doaj-art-04089685f4ce47068f2d24d4152ddcdf |
| institution | OA Journals |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-04089685f4ce47068f2d24d4152ddcdf2025-08-20T01:49:50ZengMDPI AGRemote Sensing2072-42922025-04-01179158610.3390/rs17091586SCF-CIL: A Multi-Stage Regularization-Based SAR Class-Incremental Learning Method Fused with Electromagnetic Scattering FeaturesYunpeng Zhang0Mengdao Xing1Jinsong Zhang2Sergio Vitale3The School of Electronic Engineering, Xidian University, Xi’an 710071, ChinaThe National Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, ChinaThe Academy of Advanced Interdisciplinary Research, Xidian University, Xi’an 710071, ChinaThe Dipartimento di Ingegneria, University of Naples Parthenope, 80143 Naples, ItalySynthetic aperture radar (SAR) recognition systems often need to collect new data and update the network accordingly. However, the network faces the challenge of catastrophic forgetting, where previously learned knowledge might be lost during the incremental learning of new data. To improve the applicability and sustainability of SAR target classification methods, we propose a multi-stage regularization-based class-incremental learning (CIL) method for SAR targets, called SCF-CIL, which addresses catastrophic forgetting. This method offers three main contributions. First, for the feature extractor, we fuse the convolutional neural network features with the scattering center features using a cross-attention feature fusion structure, ensuring both the plasticity and stability of the extracted features. Next, an overfitting training strategy is applied to provide clustering space for unseen classes with an acceptable trade-off in the accuracy of the current classes. Finally, we analyze the influence of training with imbalanced data on the last fully connected layer and introduce a multi-stage regularization method by dividing the calculation of the fully connected layer into three parts and applying regularization to each. Our experiments on SAR datasets demonstrate the effectiveness of these improvements.https://www.mdpi.com/2072-4292/17/9/1586synthetic aperture radarclass-incremental learningclassificationattributed scattering center feature |
| spellingShingle | Yunpeng Zhang Mengdao Xing Jinsong Zhang Sergio Vitale SCF-CIL: A Multi-Stage Regularization-Based SAR Class-Incremental Learning Method Fused with Electromagnetic Scattering Features Remote Sensing synthetic aperture radar class-incremental learning classification attributed scattering center feature |
| title | SCF-CIL: A Multi-Stage Regularization-Based SAR Class-Incremental Learning Method Fused with Electromagnetic Scattering Features |
| title_full | SCF-CIL: A Multi-Stage Regularization-Based SAR Class-Incremental Learning Method Fused with Electromagnetic Scattering Features |
| title_fullStr | SCF-CIL: A Multi-Stage Regularization-Based SAR Class-Incremental Learning Method Fused with Electromagnetic Scattering Features |
| title_full_unstemmed | SCF-CIL: A Multi-Stage Regularization-Based SAR Class-Incremental Learning Method Fused with Electromagnetic Scattering Features |
| title_short | SCF-CIL: A Multi-Stage Regularization-Based SAR Class-Incremental Learning Method Fused with Electromagnetic Scattering Features |
| title_sort | scf cil a multi stage regularization based sar class incremental learning method fused with electromagnetic scattering features |
| topic | synthetic aperture radar class-incremental learning classification attributed scattering center feature |
| url | https://www.mdpi.com/2072-4292/17/9/1586 |
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