分离难度与噪声：一种基于不确定性驱动的模型无关框架用于长尾遥感分类
Disentangling Hardness from Noise: An Uncertainty-Driven Model-Agnostic Framework for Long-Tailed Remote Sensing Classification
AI总结：
本文提出DUAL框架，通过分离预测不确定性为信念和偶然不确定性，解决遥感长尾分类中噪声与困难样本的分离问题，提升模型鲁棒性。
摘要：
长尾分布在遥感中普遍存在，由于地面物体的固有不平衡出现。然而，一个关键挑战仍被忽视，即分离困难的尾数据样本与噪声模糊样本。传统方法往往 indiscriminately 强调所有低置信度样本，导致在噪声数据上过拟合。为弥合这一差距，基于证据深度学习，我们提出了一种模型无关的不确定性感知框架，称为DUAL，动态地将预测不确定性分解为信念不确定性（EU）和偶然不确定性（AU）。具体来说，我们引入EU作为样本稀缺的指标，指导对难以学习的尾样本进行重新加权策略，同时利用AU量化数据模糊性，采用自适应标签平滑机制来抑制噪声的影响。在多个数据集上广泛实验，展示了我们框架的有效性和泛化能力，超越了TGN和SADE等强大基线。消融研究进一步揭示了我们设计中关键选择的见解。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
