用自监督学习提升湿地遥感分类精度，解决标注数据少难题
Supervised and self-supervised land-cover segmentation & classification of the Biesbosch wetlands
- 结合自监督预训练与有监督微调，提升低标注数据下的分类性能
- 在荷兰六处湿地实现88.23%准确率，高分辨率图像边界更清晰
- 开源含动态世界标签的哨兵-2数据集，支持湿地区域研究
准确的湿地地表覆盖分类对环境监测、生物多样性评估和可持续生态系统管理至关重要。然而，高质量遥感影像的标注数据稀缺，制约了有监督学习的应用。本文提出一种结合有监督与自监督学习（SSL）的湿地地表覆盖分割与分类方法。基于荷兰六处湿地的哨兵-2影像，从零开始训练U-Net模型，基线准确率达85.26%。针对标注数据有限的问题，采用自编码器进行自监督预训练，显著提升高分辨率影像分类准确率至88.23%。此外，提出一个将人工标注的高分辨率标签映射到中分辨率输入的框架，虽定量表现相近，但高分辨率图像提供更锐利的分割边界与更精细的空间细节。本研究还公开了一个经过筛选的哨兵-2数据集，包含动态世界（Dynamic World）标签，专为湿地区域分类任务设计。
原文摘要 · Abstract (English)
Accurate wetland land-cover classification is essential for environmental monitoring, biodiversity assessment, and sustainable ecosystem management. However, the scarcity of annotated data, especially for high-resolution satellite imagery, poses a significant challenge for supervised learning approaches. To tackle this issue, this study presents a methodology for wetland land-cover segmentation and classification that adopts both supervised and self-supervised learning (SSL). We train a U-Net model from scratch on Sentinel-2 imagery across six wetland regions in the Netherlands, achieving a baseline model accuracy of 85.26%. Addressing the limited availability of labeled data, the results show that SSL pretraining with an autoencoder can improve accuracy, especially for the high-resolution imagery where it is more difficult to obtain labeled data, reaching an accuracy of 88.23%. Furthermore, we introduce a framework to scale manually annotated high-resolution labels to medium-resolution inputs. While the quantitative performance between resolutions is comparable, high-resolution imagery provides significantly sharper segmentation boundaries and finer spatial detail. As part of this work, we also contribute a curated Sentinel-2 dataset with Dynamic World labels, tailored for wetland classification tasks and made publicly available.
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