用伪标签与生成学习提升遥感分割模型跨传感器泛化能力
A Sensor Agnostic Domain Generalization Framework for Leveraging Geospatial Foundation Models: Enhancing Semantic Segmentation viaSynergistic Pseudo-Labeling and Generative Learning
- 结合软对齐伪标签与生成预训练，实现跨传感器域泛化
- 在高光谱与多光谱数据上分割准确率显著提升
- 适合遥感图像分割、地理信息分析等场景研究者参考
遥感技术广泛应用于土地覆盖与利用制图、作物产量预测和环境监测。卫星技术的进步扩大了遥感数据集规模，但高性能分割模型仍依赖大量标注数据，面临标注稀缺及传感器、光照、地理区域差异的挑战。领域自适应为提升模型泛化能力提供了可行路径。本文提出一种领域泛化方法，通过融合软对齐伪标签与源到目标的生成预训练，有效利用新兴的地理空间基础模型。我们进一步为基于MAE的生成学习提供了新的数学洞察，促进域不变特征学习。在高光谱与多光谱遥感数据集上的实验验证了该方法在增强适应性和分割性能方面的有效性。
原文摘要 · Abstract (English)
Remote sensing enables a wide range of critical applications such as land cover and land use mapping, crop yield prediction, and environmental monitoring. Advances in satellite technology have expanded remote sensing datasets, yet high-performance segmentation models remain dependent on extensive labeled data, challenged by annotation scarcity and variability across sensors, illumination, and geography. Domain adaptation offers a promising solution to improve model generalization. This paper introduces a domain generalization approach to leveraging emerging geospatial foundation models by combining soft-alignment pseudo-labeling with source-to-target generative pre-training. We further provide new mathematical insights into MAE-based generative learning for domain-invariant feature learning. Experiments with hyperspectral and multispectral remote sensing datasets confirm our method's effectiveness in enhancing adaptability and segmentation.
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