IncSAR: A Dual Fusion Incremental Learning Framework for SAR Target Recognition

IF 3.4 3区 计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
George Karantaidis;Athanasios Pantsios;Ioannis Kompatsiaris;Symeon Papadopoulos
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引用次数: 0

Abstract

Deep learning techniques have achieved significant success in Synthetic Aperture Radar (SAR) target recognition using predefined datasets in static scenarios. However, real-world applications demand that models incrementally learn new information without forgetting previously acquired knowledge. The challenge of catastrophic forgetting, where models lose past knowledge when adapting to new tasks, remains a critical issue. In this paper, we introduce IncSAR, an incremental learning framework designed to tackle catastrophic forgetting in SAR target recognition. IncSAR combines the power of a Vision Transformer (ViT) and a custom-designed Convolutional Neural Network (CNN) in a dual-branch architecture, integrated via a late-fusion strategy. Additionally, we explore the use of TinyViT to reduce computational complexity and propose an attention mechanism to dynamically enhance feature representation. To mitigate the speckle noise inherent in SAR images, we employ a denoising module based on a neural network approximation of Robust Principal Component Analysis (RPCA), leveraging a simple neural network for efficient noise reduction in SAR imagery. Moreover, a random projection layer improves the linear separability of features, and a variant of Linear Discriminant Analysis (LDA) decorrelates extracted class prototypes for better generalization. Extensive experiments on the MSTAR, SAR-AIRcraft-1.0, and OpenSARShip benchmark datasets demonstrate that IncSAR significantly outperforms state-of-the-art approaches, achieving a 99.63% average accuracy and a 0.33% performance drop, representing an 89% improvement in retention compared to existing techniques. The source code is available at https://github.com/geokarant/IncSAR.
insar: SAR目标识别的双融合增量学习框架
深度学习技术在合成孔径雷达(SAR)目标识别中取得了显著的成功。然而,现实世界的应用程序要求模型在不忘记先前获得的知识的情况下增量地学习新信息。灾难性遗忘的挑战,即模型在适应新任务时失去过去的知识,仍然是一个关键问题。在本文中,我们介绍了IncSAR,一个旨在解决SAR目标识别中的灾难性遗忘的增量学习框架。IncSAR在双分支架构中结合了视觉变压器(ViT)和定制设计的卷积神经网络(CNN)的功能,并通过后期融合策略进行集成。此外,我们探索了使用TinyViT来降低计算复杂度,并提出了一种关注机制来动态增强特征表示。为了减轻SAR图像中固有的斑点噪声,我们采用了基于鲁棒主成分分析(RPCA)的神经网络近似的去噪模块,利用简单的神经网络有效地降低SAR图像中的噪声。此外,随机投影层提高了特征的线性可分性,线性判别分析(LDA)的一种变体去关联提取的类原型,以更好地泛化。在MSTAR、SAR-AIRcraft-1.0和OpenSARShip基准数据集上进行的大量实验表明,IncSAR显著优于最先进的方法,平均准确率达到99.63%,性能下降0.33%,与现有技术相比,保留率提高了89%。源代码可从https://github.com/geokarant/IncSAR获得。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
IEEE Access
IEEE Access COMPUTER SCIENCE, INFORMATION SYSTEMSENGIN-ENGINEERING, ELECTRICAL & ELECTRONIC
CiteScore
9.80
自引率
7.70%
发文量
6673
审稿时长
6 weeks
期刊介绍: IEEE Access® is a multidisciplinary, open access (OA), applications-oriented, all-electronic archival journal that continuously presents the results of original research or development across all of IEEE''s fields of interest. IEEE Access will publish articles that are of high interest to readers, original, technically correct, and clearly presented. Supported by author publication charges (APC), its hallmarks are a rapid peer review and publication process with open access to all readers. Unlike IEEE''s traditional Transactions or Journals, reviews are "binary", in that reviewers will either Accept or Reject an article in the form it is submitted in order to achieve rapid turnaround. Especially encouraged are submissions on: Multidisciplinary topics, or applications-oriented articles and negative results that do not fit within the scope of IEEE''s traditional journals. Practical articles discussing new experiments or measurement techniques, interesting solutions to engineering. Development of new or improved fabrication or manufacturing techniques. Reviews or survey articles of new or evolving fields oriented to assist others in understanding the new area.
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