Rahman Noor, Khan Muzammil, Khan Imran, Khan Jawad, Lee Youngmoon
Department of Computer Science, Virtual University, Islamabad, VIBD01, Pakistan.
Department of Computer & Software Technology, University of Swat, Swat, 01923, Pakistan.
Sci Rep. 2025 Apr 1;15(1):11053. doi: 10.1038/s41598-025-93536-x.
This paper presents an enhanced ensemble classification framework for Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR) under diverse operational conditions, including Standard Operating Conditions (SOC) and Extended Operating Conditions (EOC). The proposed method integrates the strengths of Residual Neural Networks (ResNet) replacing Convolutional Neural Networks (CNN), Support Vector Machines (SVM), and template matching, leveraging majority voting to combine their complementary capabilities. The ensemble framework achieves improved robustness and classification accuracy across varied scenarios. The methodology employs ResNet, a deep learning architecture known for its superior feature extraction and classification capabilities, replacing AlexNet to address limitations in generalization and consistency. ResNet demonstrated better performance with average accuracies of 92.67% under SOC and 88.9% under EOC, showing consistent results across all six target classes, as compared to the CNN-based ensemble approach with average accuracies of 90.30% under SOC and 87.22% under EOC. The SVM is employed for its robustness in handling overfitting and classifying features extracted from 16 region properties. Template matching is included for its resilience in challenging conditions where deep learning techniques may underperform. Experimental validation using the MSTAR dataset, a standard benchmark for SAR ATR, highlights the effectiveness of this ensemble approach. The results confirm significant improvements in classification accuracy and robustness over individual classifiers, demonstrating the practical applicability of the ensemble approach to real-world SAR ATR challenges. This research advances SAR ATR by addressing critical challenges, including noise, occlusion, and variations in viewing angles while achieving high classification performance under diverse conditions. The integration of ResNet further enhances the framework's adaptability and reliability.
本文提出了一种增强的集成分类框架，用于在包括标准操作条件（SOC）和扩展操作条件（EOC）在内的各种操作条件下进行合成孔径雷达（SAR）自动目标识别（ATR）。所提出的方法整合了残差神经网络（ResNet）取代卷积神经网络（CNN）、支持向量机（SVM）和模板匹配的优势，利用多数投票来结合它们的互补能力。该集成框架在各种场景下实现了更高的鲁棒性和分类准确率。该方法采用了以其卓越的特征提取和分类能力而闻名的深度学习架构ResNet，取代AlexNet以解决泛化和一致性方面的局限性。与基于CNN的集成方法相比，ResNet表现出更好的性能，在SOC下平均准确率为92.67%，在EOC下为88.9%，在所有六个目标类别中都显示出一致的结果，而基于CNN的集成方法在SOC下平均准确率为90.30%，在EOC下为87.22%。SVM因其在处理过拟合和对从16个区域属性中提取的特征进行分类方面的鲁棒性而被采用。模板匹配因其在深度学习技术可能表现不佳的具有挑战性的条件下的弹性而被纳入。使用MSTAR数据集（SAR ATR的标准基准）进行的实验验证突出了这种集成方法的有效性。结果证实了与单个分类器相比，分类准确率和鲁棒性有显著提高，证明了该集成方法在实际SAR ATR挑战中的实际适用性。这项研究通过解决包括噪声、遮挡和视角变化等关键挑战，同时在不同条件下实现高分类性能，推动了SAR ATR的发展。ResNet的集成进一步增强了框架的适应性和可靠性。
