发表机构
University of Helsinki（赫尔辛基大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究探索自监督学习在高分辨率多光谱无人机影像用于精准农业的有效性，用MoCo-v3等对基于Transformer的编码器预训练，在作物-杂草语义分割任务中表现出色，展示跨传感器和区域泛化能力，还提供芬兰多光谱无人机数据集。
AI 中文摘要
尽管自监督学习为减少近程遥感中的标注工作提供了一种有前景的方法，但由于数据有限，其在高分辨率多光谱无人机影像上的有效性仍未得到充分探索。本研究使用跨多个传感器、年份和地区收集的厘米级多光谱无人机影像，评估了用于精准农业的自监督学习预训练。基于Transformer的编码器在一个将msuav500K与新收集的芬兰农田多年无人机影像相结合的统一数据集上，使用动量对比v3（MoCo-v3）和掩码自动编码器进行预训练。预训练使用四个光谱带（绿色、红色、红边、近红外）以实现跨传感器兼容性。模型在使用WeedMap数据集且训练数据比例为5%-100%的作物-杂草语义分割任务上进行评估。有两个子集作为下游任务：任务A（德国，红边-M），在部分和完全微调下比较所有预训练模型；任务B（瑞士，红杉），评估任务A中最佳编码器。我们用MoCo-v3预训练的Swin Transformer在两个任务上都取得了最强性能，超过了在msuav500K预发布版本上预训练的Doornbos等人的Swin Transformer模型。我们预训练的Swin Transformer还展示了跨传感器和跨区域的泛化能力。此外，我们提供了一个来自芬兰的公共多年多光谱无人机数据集以支持未来研究。
英文摘要
Although self-supervised learning (SSL) offers a promising way to reduce annotation effort in close-range remote sensing, its effectiveness for high-resolution multispectral unmanned aerial vehicle (UAV) imagery remains underexplored due to limited data. This study evaluated SSL pretraining for precision agriculture using cm-scale multispectral drone imagery collected across multiple sensors, years, and regions. Transformer-based encoders were pretrained with Momentum Contrast v3 (MoCo-v3) and Masked Autoencoders on a harmonized dataset combining msuav500K with newly collected multi-year UAV imagery from agricultural fields in Finland. Pretraining used four spectral bands (Green, Red, Red-Edge, Near-Infrared) for cross-sensor compatibility. The models were evaluated on crop-weed semantic segmentation using the WeedMap dataset with 5--100% training data. The following two subsets served as downstream tasks: Task A (Germany, RedEdge-M), where all pretrained models were compared under partial and full fine-tuning, and Task B (Switzerland, Sequoia), where the best encoder from Task A was assessed. Our Swin Transformer pretrained with MoCo-v3 achieved the strongest performance on both tasks, surpassing the Swin Transformer model of Doornbos et al. pretrained on a pre-release of msuav500K. Our pretrained Swin Transformer further demonstrated cross-sensor and cross-region generalization. We additionally provide a public multi-year multispectral UAV dataset from Finland to support future research.
