区分难样本与噪声，提升遥感图像长尾分类效果
Disentangling Hardness from Noise: An Uncertainty-Driven Model-Agnostic Framework for Long-Tailed Remote Sensing Classification
- 用不确定性分解分离难样本和模糊噪声
- 在多个数据集上超越TGN、SADE等基线方法
- 适合遥感、长尾分布等复杂场景的分类任务
遥感图像中普遍存在长尾分布，源于地面物体出现频率的固有不平衡。然而，一个关键问题长期被忽视：如何将难以识别的尾部样本与噪声模糊样本区分开。传统方法对所有低置信度样本一概而论，导致对噪声数据过拟合。为此，基于证据深度学习，我们提出一种模型无关的不确定性感知框架DUAL，动态将预测不确定性分解为认知不确定性（EU）和随机不确定性（AU）。具体而言，利用EU作为样本稀缺性的指标，指导对难学尾部样本的重加权策略；同时借助AU量化数据模糊性，采用自适应标签平滑机制抑制噪声影响。在多种骨干网络和多个数据集上的大量实验表明，该框架具有显著有效性与泛化能力，优于TGN、SADE等强基线。消融研究进一步揭示了设计选择的关键作用。
原文摘要 · Abstract (English)
Long-Tailed distributions are pervasive in remote sensing due to the inherently imbalanced occurrence of grounded objects. However, a critical challenge remains largely overlooked, i.e., disentangling hard tail data samples from noisy ambiguous ones. Conventional methods often indiscriminately emphasize all low-confidence samples, leading to overfitting on noisy data. To bridge this gap, building upon Evidential Deep Learning, we propose a model-agnostic uncertainty-aware framework termed DUAL, which dynamically disentangles prediction uncertainty into Epistemic Uncertainty (EU) and Aleatoric Uncertainty (AU). Specifically, we introduce EU as an indicator of sample scarcity to guide a reweighting strategy for hard-to-learn tail samples, while leveraging AU to quantify data ambiguity, employing an adaptive label smoothing mechanism to suppress the impact of noise. Extensive experiments on multiple datasets across various backbones demonstrate the effectiveness and generalization of our framework, surpassing strong baselines such as TGN and SADE. Ablation studies provide further insights into the crucial choices of our design.
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