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.

翻译：遥感技术通过将语义分割技术应用于卫星图像，显著推进了水体检测的发展。然而，由于需要大量标注数据，语义分割仍然面临挑战。这一问题在湿地水体检测中尤为突出，因为水体范围在时间和空间上不断变化，导致同一区域需要多次标注。本文提出DeepAqua，一种利用知识蒸馏在训练阶段消除人工标注需求的自监督深度学习模型。DeepAqua采用归一化水体指数（NDWI）作为教师模型，训练卷积神经网络（CNN）从合成孔径雷达（SAR）图像中分割水体。为训练学生模型，我们利用光学与雷达水体制图结果一致的情况，从而能同时检测开阔水域和植被覆盖水域。我们的模型通过无需任何人工标注数据即可有效训练语义分割模型，代表了计算机视觉技术的重要进展。该方法无需真实地面数据即可监测湿地水体范围变化，为湿地保护工作提供了高度适应性和可扩展性的实用解决方案。