用自监督学习从雷达影像中无标注检测植被下的水体
AquaCluster: Using Satellite Images And Self-supervised Machine Learning Networks To Detect Water Hidden Under Vegetation
- 通过自监督方法训练模型，无需人工标注数据
- 在测试集上交并比指标提升0.08，优于其他无标注方法
- 适合需要快速适应新气候或传感器的水体监测场景
近年来，高分辨率雷达卫星影像的广泛可用使得湿地表面积的遥感监测成为可能。机器学习模型在分割卫星影像中的湿地方面已取得领先成果。然而，这些模型需要大量人工标注的卫星图像作为训练数据，而这类数据的生成耗时且成本高昂。标注数据的需求使模型难以适应不同气候或传感器的变化。为解决此问题，我们采用自监督训练方法开发了AquaCluster模型，该模型可在无需人工标注的情况下，将雷达卫星影像分割为水体与陆地区域。在测试数据集上，我们的最终模型表现优于其他无需标注数据的雷达水体检测技术，交并比（Intersection over Union）指标提升了0.08。结果表明，无需标注数据即可训练机器学习模型来检测植被覆盖下的水体，这将使模型应对环境变化时的重新训练更加便捷。
原文摘要 · Abstract (English)
In recent years, the wide availability of high-resolution radar satellite images has enabled the remote monitoring of wetland surface areas. Machine learning models have achieved state-of-the-art results in segmenting wetlands from satellite images. However, these models require large amounts of manually annotated satellite images, which are slow and expensive to produce. The need for annotated training data makes it difficult to adapt these models to changes such as different climates or sensors. To address this issue, we employed self-supervised training methods to develop a model, AquaCluster, which segments radar satellite images into water and land areas without manual annotations. Our final model outperformed other radar-based water detection techniques that do not require annotated data in our test dataset, having achieved a 0.08 improvement in the Intersection over Union metric. Our results demonstrate that it is possible to train machine learning models to detect vegetated water from radar images without the use of annotated data, which can make the retraining of these models to account for changes much easier.
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