用更少的标签：利用自监督深度学习进行非常高的空间分辨率的标签高效土地覆盖分类
Learning with less: label-efficient land cover classification at very high spatial resolution using self-supervised deep learning
AI总结：
本研究提出一种基于自监督深度学习的标签高效方法，利用少量标注数据实现高分辨率土地覆盖分类，取得87.14%的总体准确率。
摘要：
深度学习语义分割方法在非常高的1米分辨率土地覆盖分类中显示出有希望的性能，但收集大量代表性训练数据的挑战成为此类模型在大范围内广泛应用的显著障碍。在本研究中，我们提出了一种新的标签高效方法，使用仅1,000个标注参考图像块的自监督深度学习，用于州级1米土地覆盖分类。我们使用
英文摘要：
Deep learning semantic segmentation methods have shown promising performance for very high 1-m resolution land cover classification, but the challenge of collecting large volumes of representative training data creates a significant barrier to widespread adoption of such models for meter-scale land cover mapping over large areas. In this study, we present a novel label-efficient approach for statewide 1-m land cover classification using only 1,000 annotated reference image patches with self-supervised deep learning. We use the "Bootstrap Your Own Latent" pre-training strategy with a large amount of unlabeled color-infrared aerial images (377,921 patches of 256x256 pixels at 1-m resolution) to pre-train a ResNet-101 convolutional encoder. The learned encoder weights were subsequently transferred into multiple deep semantic segmentation architectures (FCN, U-Net, Attention U-Net, DeepLabV3+, UPerNet, PAN), which were then fine-tuned using very small training dataset sizes with cross-validation (250, 500, 750 patches). Among the fine-tuned models, we obtained 87.14% overall accuracy and 75.58% macro F1 score using an ensemble of the best-performing U-Net models for comprehensive 1-m, 8-class land cover mapping, covering more than 123 billion pixels over the state of Mississippi, USA. Detailed qualitative and quantitative analysis revealed accurate mapping of open water and forested areas, while highlighting challenges in accurate delineation between cropland, herbaceous, and barren land cover types. These results show that self-supervised learning is an effective strategy for reducing the need for large volumes of manually annotated data, directly addressing a major limitation to high spatial resolution land cover mapping at scale.
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。
