Few-Shot Class-Incremental SAR Target Recognition Based on Dynamic Task-Adaptive Classifier.
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| Title: | Few-Shot Class-Incremental SAR Target Recognition Based on Dynamic Task-Adaptive Classifier. |
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| Authors: | Li, Dan1 (AUTHOR), Zhao, Feng2 (AUTHOR), Li, Yong1 (AUTHOR) ruikel@nwpu.edu.cn, Cheng, Wei1,2 (AUTHOR) |
| Source: | Remote Sensing. Feb2026, Vol. 18 Issue 3, p527. 19p. |
| Subjects: | Synthetic aperture radar, Automatic target recognition, Remote sensing, Machine learning, Feature extraction |
| Abstract: | Highlights: What are the main findings? The proposed DTAC method outperforms 13 baseline models (covering traditional deep learning, incremental learning, and FSCIL methods) on two self-constructed SAR datasets (SAR-Aircraft-1.0-FSCIL and MSTAR-FSCIL), achieving average accuracies of 86.30% and 82.12% respectively, with the lowest performance degradation rate (PD) and highest harmonic accuracy (Avg.HA of 63.16% and 75.95%). Ablation experiments confirm that the task information encoding module and classifier generation module are critical to the model's performance: integrating both modules significantly mitigates catastrophic forgetting and overfitting, with the task encoder contributing the most prominently to performance improvement. What is the implication of the main finding? For the field of SAR ATR, the dynamic task-adaptive mechanism provides a new solution to the "stability-plasticity dilemma" in few-shot class-incremental scenarios, enabling reliable recognition of new classes with limited samples while preserving prior knowledge—addressing the practical pain point of scarce annotated SAR data and evolving target classes. The modular design of DTAC (feature extraction + task encoding + dynamic classifier generation) offers a scalable framework for related incremental learning tasks, inspiring the development of task-aware adaptive models in other remote sensing image recognition fields (e.g., optical remote sensing, LiDAR) facing similar few-shot and incremental learning challenges. Current synthetic aperture radar automatic target recognition (SAR ATR) tasks face challenges including limited training samples and poor generalization capability to novel classes. To address these issues, few-shot class-incremental learning (FSCIL) has emerged as a promising research direction. Few-shot learning facilitates the expedited adaptation to novel tasks utilizing a limited number of labeled samples, whereas incremental learning concentrates on the continuous refinement of the model as new categories are incorporated without eradicating previously learned knowledge. Although both methodologies present potential resolutions to the challenges of sample scarcity and class evolution in SAR target recognition, they are not without their own set of difficulties. Fine-tuning with emerging classes can perturb the feature distribution of established classes, culminating in catastrophic forgetting, while training exclusively on a handful of new samples can induce bias towards older classes, leading to distribution collapse and overfitting. To surmount these limitations and satisfy practical application requirements, we propose a Few-Shot Class-Incremental SAR Target Recognition method based on a Dynamic Task-Adaptive Classifier (DTAC). This approach underscores task adaptability through a feature extraction module, a task information encoding module, and a classifier generation module. The feature extraction module discerns both target-specific and task-specific characteristics, while the task information encoding module modulates the network parameters of the classifier generation module based on pertinent task information, thereby improving adaptability. Our innovative classifier generation module, honed with task-specific insights, dynamically assembles classifiers tailored to the current task, effectively accommodating a variety of scenarios and novel class samples. Our extensive experiments on SAR datasets demonstrate that our proposed method generally outperforms the baselines in few-shot class incremental SAR target recognition. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? The proposed DTAC method outperforms 13 baseline models (covering traditional deep learning, incremental learning, and FSCIL methods) on two self-constructed SAR datasets (SAR-Aircraft-1.0-FSCIL and MSTAR-FSCIL), achieving average accuracies of 86.30% and 82.12% respectively, with the lowest performance degradation rate (PD) and highest harmonic accuracy (Avg.HA of 63.16% and 75.95%). Ablation experiments confirm that the task information encoding module and classifier generation module are critical to the model's performance: integrating both modules significantly mitigates catastrophic forgetting and overfitting, with the task encoder contributing the most prominently to performance improvement. What is the implication of the main finding? For the field of SAR ATR, the dynamic task-adaptive mechanism provides a new solution to the "stability-plasticity dilemma" in few-shot class-incremental scenarios, enabling reliable recognition of new classes with limited samples while preserving prior knowledge—addressing the practical pain point of scarce annotated SAR data and evolving target classes. The modular design of DTAC (feature extraction + task encoding + dynamic classifier generation) offers a scalable framework for related incremental learning tasks, inspiring the development of task-aware adaptive models in other remote sensing image recognition fields (e.g., optical remote sensing, LiDAR) facing similar few-shot and incremental learning challenges. Current synthetic aperture radar automatic target recognition (SAR ATR) tasks face challenges including limited training samples and poor generalization capability to novel classes. To address these issues, few-shot class-incremental learning (FSCIL) has emerged as a promising research direction. Few-shot learning facilitates the expedited adaptation to novel tasks utilizing a limited number of labeled samples, whereas incremental learning concentrates on the continuous refinement of the model as new categories are incorporated without eradicating previously learned knowledge. Although both methodologies present potential resolutions to the challenges of sample scarcity and class evolution in SAR target recognition, they are not without their own set of difficulties. Fine-tuning with emerging classes can perturb the feature distribution of established classes, culminating in catastrophic forgetting, while training exclusively on a handful of new samples can induce bias towards older classes, leading to distribution collapse and overfitting. To surmount these limitations and satisfy practical application requirements, we propose a Few-Shot Class-Incremental SAR Target Recognition method based on a Dynamic Task-Adaptive Classifier (DTAC). This approach underscores task adaptability through a feature extraction module, a task information encoding module, and a classifier generation module. The feature extraction module discerns both target-specific and task-specific characteristics, while the task information encoding module modulates the network parameters of the classifier generation module based on pertinent task information, thereby improving adaptability. Our innovative classifier generation module, honed with task-specific insights, dynamically assembles classifiers tailored to the current task, effectively accommodating a variety of scenarios and novel class samples. Our extensive experiments on SAR datasets demonstrate that our proposed method generally outperforms the baselines in few-shot class incremental SAR target recognition. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18030527 |
