Robust ensemble classifier for advanced synthetic aperture radar target classification in diverse operational conditions
Abstract 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...
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Nature Portfolio
2025-04-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-93536-x |
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| author | Noor Rahman Muzammil Khan Imran Khan Jawad Khan Youngmoon Lee |
| author_facet | Noor Rahman Muzammil Khan Imran Khan Jawad Khan Youngmoon Lee |
| author_sort | Noor Rahman |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-5432b412359d4a4aa03f48ef9e08ef8e |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
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| spelling | doaj-art-5432b412359d4a4aa03f48ef9e08ef8e2025-08-20T01:54:23ZengNature PortfolioScientific Reports2045-23222025-04-0115111810.1038/s41598-025-93536-xRobust ensemble classifier for advanced synthetic aperture radar target classification in diverse operational conditionsNoor Rahman0Muzammil Khan1Imran Khan2Jawad Khan3Youngmoon Lee4Department of Computer Science, Virtual UniversityDepartment of Computer & Software Technology, University of SwatDepartment of Computer & Software Technology, University of SwatSchool of Computing, Gachon UniversityDepartment of Robotics, Hanyang UniversityAbstract 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.https://doi.org/10.1038/s41598-025-93536-xEnsemble ClassifierSynthetic Aperture RadarAutomatic Target RecognitionStandard Operating ConditionExtended Operating ConditionImage Classification |
| spellingShingle | Noor Rahman Muzammil Khan Imran Khan Jawad Khan Youngmoon Lee Robust ensemble classifier for advanced synthetic aperture radar target classification in diverse operational conditions Scientific Reports Ensemble Classifier Synthetic Aperture Radar Automatic Target Recognition Standard Operating Condition Extended Operating Condition Image Classification |
| title | Robust ensemble classifier for advanced synthetic aperture radar target classification in diverse operational conditions |
| title_full | Robust ensemble classifier for advanced synthetic aperture radar target classification in diverse operational conditions |
| title_fullStr | Robust ensemble classifier for advanced synthetic aperture radar target classification in diverse operational conditions |
| title_full_unstemmed | Robust ensemble classifier for advanced synthetic aperture radar target classification in diverse operational conditions |
| title_short | Robust ensemble classifier for advanced synthetic aperture radar target classification in diverse operational conditions |
| title_sort | robust ensemble classifier for advanced synthetic aperture radar target classification in diverse operational conditions |
| topic | Ensemble Classifier Synthetic Aperture Radar Automatic Target Recognition Standard Operating Condition Extended Operating Condition Image Classification |
| url | https://doi.org/10.1038/s41598-025-93536-x |
| work_keys_str_mv | AT noorrahman robustensembleclassifierforadvancedsyntheticapertureradartargetclassificationindiverseoperationalconditions AT muzammilkhan robustensembleclassifierforadvancedsyntheticapertureradartargetclassificationindiverseoperationalconditions AT imrankhan robustensembleclassifierforadvancedsyntheticapertureradartargetclassificationindiverseoperationalconditions AT jawadkhan robustensembleclassifierforadvancedsyntheticapertureradartargetclassificationindiverseoperationalconditions AT youngmoonlee robustensembleclassifierforadvancedsyntheticapertureradartargetclassificationindiverseoperationalconditions |