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SAR Target Incremental Recognition Based on Features With Strong Separability

Gao, Fei; Kong, Lingzhe; Lang, Rongling; Sun, Jinping; Wang, Jun; Hussain, Amir; Zhou, Huiyu

Authors

Fei Gao

Lingzhe Kong

Rongling Lang

Jinping Sun

Jun Wang

Huiyu Zhou



Abstract

With the rapid development of deep learning technology, many synthetic aperture radar (SAR) target recognition algorithms based on convolutional neural networks have achieved exceptional performance on various datasets. However, conventional neural networks are repeatedly iterated on a fixed dataset until convergence, and once they learn new tasks, a large amount of previously learned knowledge is forgotten, leading to a significant decline in performance on old tasks. This article presents an incremental learning method based on strong separability features (SSF-IL) to address the model’s forgetting of previously learned knowledge. The SSF-IL employs both intraclass and interclass scatter to compute the feature separability loss, in order to enhance the linear separability of features during incremental learning. In the process of learning new classes, an intraclass clustering loss is proposed to replace the conventional knowledge distillation. This loss function constrains the old class features to cluster around the saved class centers, maintaining the separability among the old class features. Finally, a classifier bias correction method based on boundary features is designed to reinforce the classifier’s decision boundary and reduce classification errors. SAR target incremental recognition experiments are conducted on the MSTAR dataset, and the results are compared with several existing incremental learning algorithms to demonstrate the effectiveness of the proposed algorithm.

Journal Article Type Article
Acceptance Date Jan 2, 2024
Online Publication Date Jan 9, 2024
Publication Date 2024
Deposit Date Feb 5, 2024
Publicly Available Date Mar 4, 2024
Journal IEEE Transactions on Geoscience and Remote Sensing
Print ISSN 0196-2892
Electronic ISSN 1558-0644
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 62
Pages 1-13
DOI https://doi.org/10.1109/tgrs.2024.3351636
Keywords Bias correction, feature separability, incremen- tal learning, intraclass clustering, synthetic aperture radar (SAR) target recognition
Public URL http://researchrepository.napier.ac.uk/Output/3499993

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