光学引导的SAR少样本类增量学习中的神经坍缩
Optical-Guided Neural Collapse for SAR Few-Shot Class Incremental Learning
AI总结：
针对SAR图像少样本类增量学习中的数据稀缺和方位角敏感问题，提出利用光学ATR数据集的正交子空间作为几何先验，通过投影损失和分类器损失联合诱导神经坍缩，实现特征紧凑性和类间可分离性。
摘要：
合成孔径雷达图像中的少样本类增量学习由于严重的数据稀缺和SAR特有的变异性而面临独特挑战。特别是，SAR中强烈的方位角敏感性导致大的类内变异和类间混淆，而FSCIL的顺序更新进一步导致先前学习类别的灾难性遗忘。受神经坍缩启发，我们提出了一种光学引导的SAR FSCIL框架，该框架从数据丰富的光学ATR数据集中推导出正交特征子空间，并将其作为几何先验来指导SAR特征学习。通过主角约束将SAR特征投影到这些正交子空间上，有效地将判别结构从光学域转移到SAR域。具体地，我们的投影损失和用冻结的单纯形ETF几何优化的分类器损失通过将特征集中在类均值周围同时保持大的类间角度，联合诱导神经坍缩。我们在一个包含光学ATR数据集和具有24个目标类别的SAR ATR数据集的基准上评估该方法，该基准组织为一个基础训练会话和七个增量会话。与最近的FSCIL方法（包括NCFSCIL等）相比，我们的方法实现了最高的最终准确率以及最终性能与性能下降之间的有利权衡。此外，神经坍缩指标显示类内紧凑性和类间可分离性得到改善，表明学习到的特征更接近理想的单纯形ETF几何。
英文摘要：
Few-shot class-incremental learning (FSCIL) in synthetic aperture radar imagery presents unique challenges due to severe data scarcity and SAR-specific variability. In particular, strong azimuth sensitivity in SAR induces large intra-class variation and inter-class confusion, and FSCIL sequential updates further lead to catastrophic forgetting of previously learned classes. Inspired by neural collapse, we propose an optical-guided SAR FSCIL framework, which derives orthogonal feature subspaces from a data-rich optical ATR dataset and uses them as geometric priors to guide SAR feature learning. SAR features are projected onto these orthogonal subspaces via principal angle constraints, effectively transferring discriminative structure from the optical to the SAR domain. Specifically, our projection loss and the classifier loss optimized with a frozen simplex-ETF geometry jointly induce neural collapse by concentrating features around class means while maintaining large inter-class angles. We evaluate the approach on a benchmark comprising an optical ATR dataset and a SAR ATR dataset with 24 target classes, organized into a base training session and seven incremental sessions. Compared with recent FSCIL methods including NCFSCIL and so on, our method achieves the highest final accuracy and a favorable trade-off between final performance and performance degradation. Moreover, neural collapse metrics show improved intra-class compactness and inter-class separability, indicating that the learned features more closely approximate the ideal simplex-ETF geometry.
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