AquaCluster: Using Satellite Images And Self-supervised Machine Learning Networks To Detect Water Hidden Under Vegetation
This addresses the challenge of expensive and slow manual annotations for wetland monitoring, making it easier to adapt models to different climates or sensors, though it is incremental as it builds on existing self-supervised methods.
The paper tackled the problem of detecting water hidden under vegetation in satellite images without requiring manually annotated data, achieving a 0.08 improvement in Intersection over Union over other annotation-free methods.
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.