用扩散模型生成多样遥感图像，解决标注数据少的问题。
EarthSynth: Generating Informative Earth Observation with Diffusion Models
- 基于扩散模型合成多类别跨卫星遥感图像。
- 在开放词汇任务中显著提升场景分类等性能。
- 适合需要大量标注数据的遥感分析研究者。
遥感图像（RSI）解释通常受限于标注数据稀缺，影响下游任务表现。为此，我们提出EarthSynth，一种基于扩散的生成基础模型，可为下游遥感解释任务合成多类别、跨卫星的带标签地球观测数据。据我们所知，EarthSynth是首个探索遥感多任务生成的模型，解决了任务导向合成中泛化能力不足的问题。EarthSynth在EarthSynth-180K数据集上训练，采用反事实组合训练策略与三维批次-样本选择机制，提升训练数据多样性并增强类别控制能力。此外，提出R-Filter规则方法筛选更具信息量的合成数据用于下游任务。我们在开放世界场景下评估了EarthSynth在场景分类、目标检测和语义分割的表现，显著提升了开放词汇理解任务效果，为推进遥感图像解释提供了实用方案。
原文摘要 · Abstract (English)
Remote sensing image (RSI) interpretation typically faces challenges due to the scarcity of labeled data, which limits the performance of RSI interpretation tasks. To tackle this challenge, we propose EarthSynth, a diffusion-based generative foundation model that enables synthesizing multi-category, cross-satellite labeled Earth observation for downstream RSI interpretation tasks. To the best of our knowledge, EarthSynth is the first to explore multi-task generation for remote sensing, tackling the challenge of limited generalization in task-oriented synthesis for RSI interpretation. EarthSynth, trained on the EarthSynth-180K dataset, employs the Counterfactual Composition training strategy with a three-dimensional batch-sample selection mechanism to improve training data diversity and enhance category control. Furthermore, a rule-based method of R-Filter is proposed to filter more informative synthetic data for downstream tasks. We evaluate our EarthSynth on scene classification, object detection, and semantic segmentation in open-world scenarios. There are significant improvements in open-vocabulary understanding tasks, offering a practical solution for advancing RSI interpretation.
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