AquaCluster: Using Satellite Images And Self-supervised Machine Learning Networks To Detect Water Hidden Under Vegetation

TL;DR

AquaCluster leverages self-supervised learning to accurately detect water under vegetation in satellite radar images without needing manual annotations, improving adaptability to different conditions.

Contribution

This paper introduces AquaCluster, a novel self-supervised model for water detection in satellite images that eliminates the need for manual annotations, enhancing flexibility across various environments.

Findings

Outperformed other radar-based water detection methods

Achieved a 0.08 improvement in IoU metric

Demonstrated effective detection of vegetated water without annotations

Abstract

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.