跨数据集迁移与未知类别检测：不平衡SAR舰船分类
Cross-Dataset Transfer and Unknown-Class Detection in Imbalanced SAR Ship Classification
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中文总结 AI 辅助
本研究评估六个预训练模型在SAR舰船分类中的域内、跨数据集迁移及未知类别检测性能，发现跨数据集泛化有限，任务特定不确定性分数优于MC-dropout方差。
中文摘要 AI 辅助
基于合成孔径雷达（SAR）图像的舰船分类是一项关键的计算机视觉任务，然而模型在部署环境变化下的鲁棒性仍不明确。尽管模型通常在一个数据集上训练并在另一个数据集上部署，我们对其跨数据集泛化能力缺乏全面理解。为解决这一问题，我们在两个SAR舰船数据集上评估了六个预训练模型，涵盖三种设置：域内分类、跨数据集迁移和未知类别检测。对于未知类别检测，我们逐一将所有类别设为留出类别。在域内设置中，SARDet100K在两个数据集上均取得了最佳平衡准确率（在FUSARShip上为73.4%，在OpenSARShip上为53.2%）。在跨数据集迁移中，我们观察到严重的失败：一些模型表现出中等准确率但接近随机的平衡准确率（例如，从OpenSARShip到FUSARShip时准确率为64.5%，但平衡准确率仅为33.3%）。在未知类别检测中，性能取决于留出类别和数据集，而MC-dropout方差通常接近随机水平。这些发现表明，SAR中的跨数据集泛化仍然有限，且对于留出类别检测，任务特定的不确定性分数通常比MC-dropout方差更具信息量，尽管它们的相对排名取决于数据集和留出类别。
英文摘要
Ship classification from Synthetic Aperture Radar (SAR) imagery is a critical computer vision task, yet the robustness of models under deployment shifts remains unclear. While models are often trained on one dataset and deployed on another, we lack a comprehensive understanding of their cross-dataset generalization. To address this, we evaluate six pretrained models on two SAR ship datasets in three settings: in-domain classification, cross-dataset transfer, and unknown-class detection. For unknown detection, we hold out all classes one at a time. In-domain, SARDet100K gives the best balanced accuracy on both datasets (73.4\% on FUSARShip and 53.2\% on OpenSARShip). In cross-dataset transfer, we observe strong failures: some models show moderate accuracy but near-chance balanced accuracy (for example, 64.5\% accuracy but 33.3\% balanced accuracy for OpenSARShip to FUSARShip). In unknown detection, performance depends on the held-out class and dataset, while MC-dropout variance is often close to random. These findings show that cross-dataset generalization in SAR remains limited and that task-specific uncertainty scores are often more informative than MC-dropout variance for held-out-class detection, although their relative ranking depends on the dataset and held-out class.
