发表机构
North Carolina State University（北卡罗来纳州立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文首次针对遥感变化检测开展数据剪枝基准测试，发现现有基线无可靠优势，提出FDC两阶段剪枝方法，在变化检测任务上性能优于现有基线，尤其在低剪枝比例下表现突出。
AI 中文摘要
尽管数据剪枝（DP）在减小分类和分割任务的训练数据规模、提升下游模型性能方面已取得成功，但其在遥感变化检测领域的潜力仍未被探索。本文首次针对建筑和森林变化数据集、基于CNN和Transformer的模型，以及三种剪枝预算，对六种代表性DP方法进行基准测试，结果显示现有基线方法相比随机选择无可靠优势；值得注意的是，即使是评估中表现最强的基线方法——特征多样性，也有约33%的随机采样子集可与之匹敌或超越。为探究内在机制，本文对540个随机采样数据子集开展系统回归研究，用四个描述符对每个子集进行表征，涵盖标签统计、图像多样性和特征空间几何；随机森林模型显示，“变化分布保真度”是决定变化检测数据子集质量的最关键因素，该特性在现有剪枝文献中未被涉及。分析进一步表明，像素级图像多样性和标签-特征一致性是次要因素。本文将上述发现转化为Fidelity-Diversity-Consistency（FDC）方法，这是一种简单的两阶段剪枝方法，在变化检测基准和骨干网络上均表现出优于现有基线的一致改进，尤其在较低剪枝比例下效果显著。代码可访问：this https URL。
英文摘要
Despite the success of data pruning (DP) in reducing training data sizes and improving downstream model performance in classification and segmentation tasks, its potential in remote sensing change detection remains unexplored. For the first time, we benchmark six representative DP methods across building- and forest-change datasets, CNN- and transformer-based models, and three pruning budgets, and show that existing baselines yield no reliable advantage over random selection. Notably, even the strongest evaluated baseline, Feature Diversity, is matched or exceeded by $\sim$33\% of randomly sampled subsets. To understand the underlying mechanism, we conduct a systematic regression study over 540 randomly sampled data subsets, characterizing each with four descriptors covering label statistics, image diversity, and feature-space geometry. Random Forest models show that \emph{change distribution fidelity} is the most prominent factor in determining the quality of change detection data subsets, a property absent from the existing pruning literature. Our analyses further show that pixel-wise image diversity and label-feature consistency are secondary factors. We translate these findings into Fidelity-Diversity-Consistency (FDC), a simple two-stage pruning method that shows consistent improvements over existing baselines across change detection benchmarks and backbones, especially at lower pruning ratios. Code is available at \href{https://github.com/ddydyd32/fidelity-diversity-consistency}{https://github.com/ddydyd32/fidelity-diversity-consistency}.
