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

In recent years the wide availability of high-resolution radar satellite images along with the advancement of computer vision models have enabled the remote monitoring of the surface area of wetlands. However, these models require large amounts of manually annotated satellite images, which are slow and expensive to produce. To overcome this problem, self-supervised training methods have been deployed to train models without using annotated data. In this paper we use a combination of deep clustering and negative sampling to train a model to segment radar satellite images into areas that separate water from land without the use of any manual annotations. Furthermore, we implement an ensemble version of the model to reduce variance and improve performance. Compared to a single fully-supervised model using the same architecture, our ensemble of self-supervised models achieves a 0.02 improvement in the Intersection Over Union metric over our test dataset.

DeepAqua: Self-Supervised Semantic Segmentation of Wetlands from SAR Images using Knowledge Distillation

Remote sensing has significantly advanced water detection by applying semantic segmentation techniques to satellite imagery. However, semantic segmentation remains challenging due to the substantial amount of annotated data required. This is particularly problematic in wetland detection, where water extent varies over time and space, necessitating multiple annotations for the same area. In this paper, we present DeepAqua, a self-supervised deep learning model that leverages knowledge distillation to eliminate the need for manual annotations during the training phase. DeepAqua utilizes the Normalized Difference Water Index (NDWI) as a teacher model to train a Convolutional Neural Network (CNN) for segmenting water from Synthetic Aperture Radar (SAR) images. To train the student model, we exploit cases where optical- and radar-based water masks coincide, enabling the detection of both open and vegetated water surfaces. Our model represents a significant advancement in computer vision techniques by effectively training semantic segmentation models without any manually annotated data. This approach offers a practical solution for monitoring wetland water extent changes without needing ground truth data, making it highly adaptable and scalable for wetland conservation efforts.