We present a new method to adapt an RGB-trained water segmentation network to target-domain aerial thermal imagery using online self-supervision by leveraging texture and motion cues as supervisory signals. This new thermal capability enables current autonomous aerial robots operating in near-shore environments to perform tasks such as visual navigation, bathymetry, and flow tracking at night. Our method overcomes the problem of scarce and difficult-to-obtain near-shore thermal data that prevents the application of conventional supervised and unsupervised methods. In this work, we curate the first aerial thermal near-shore dataset, show that our approach outperforms fully-supervised segmentation models trained on limited target-domain thermal data, and demonstrate real-time capabilities onboard an Nvidia Jetson embedded computing platform. Code and datasets used in this work will be available at: https://github.com/connorlee77/uav-thermal-water-segmentation.
@article{arxiv.2307.09027,
title = {Online Self-Supervised Thermal Water Segmentation for Aerial Vehicles},
author = {Connor Lee and Jonathan Gustafsson Frennert and Lu Gan and Matthew Anderson and Soon-Jo Chung},
journal= {arXiv preprint arXiv:2307.09027},
year = {2023}
}