# Online Self-Supervised Thermal Water Segmentation for Aerial Vehicles

arXiv: https://arxiv.org/abs/2307.09027 · HTML: https://mlsanity.com/paper/2307.09027/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 47 references that are in the map.
- First posted on arXiv 18 Jul 2023.

## Builds on (most cited first, 20 of 20)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Exploring Simple Siamese Representation Learning](https://mlsanity.com/paper/2011.10566/) (2020)
- [Searching for MobileNetV3](https://mlsanity.com/paper/1905.02244/) (2019)
- [Semantic Understanding of Scenes through the ADE20K Dataset](https://mlsanity.com/paper/1608.05442/) (2016)
- [COCO-Stuff: Thing and Stuff Classes in Context](https://mlsanity.com/paper/1612.03716/) (2016)
- [Contrastive Learning for Unpaired Image-to-Image Translation](https://mlsanity.com/paper/2007.15651/) (2020)
- [Self-supervised Augmentation Consistency for Adapting Semantic Segmentation](https://mlsanity.com/paper/2105.00097/) (2021)
- [Fast-SCNN: Fast Semantic Segmentation Network](https://mlsanity.com/paper/1902.04502/) (2019)
- [Multispectral Fusion for Object Detection with Cyclic Fuse-and-Refine Blocks](https://mlsanity.com/paper/2009.12664/) (2020)
- [Segmenting Objects in Day and Night:Edge-Conditioned CNN for Thermal Image Semantic Segmentation](https://mlsanity.com/paper/1907.10303/) (2019)
- [Synthetic data generation for end-to-end thermal infrared tracking](https://mlsanity.com/paper/1806.01013/) (2018)
- [Fast image-based obstacle detection from unmanned surface vehicles](https://mlsanity.com/paper/1503.01918/) (2015)
- [Self-Supervised Traversability Prediction by Learning to Reconstruct Safe Terrain](https://mlsanity.com/paper/2208.01329/) (2022)
- [Stereo obstacle detection for unmanned surface vehicles by IMU-assisted semantic segmentation](https://mlsanity.com/paper/1802.07956/) (2018)
- [Unsupervised RGB-to-Thermal Domain Adaptation via Multi-Domain Attention Network](https://mlsanity.com/paper/2210.04367/) (2022)
- [Self-training Guided Adversarial Domain Adaptation For Thermal Imagery](https://mlsanity.com/paper/2106.07165/) (2021)
- [MassMIND: Massachusetts Maritime INfrared Dataset](https://mlsanity.com/paper/2209.04097/) (2022)
- [A water-obstacle separation and refinement network for unmanned surface vehicles](https://mlsanity.com/paper/2001.01921/) (2020)

## Built on by (most cited first, 6 of 6)

- [Caltech Aerial RGB-Thermal Dataset in the Wild](https://mlsanity.com/paper/2403.08997/) (2024)
- [RGB-X Object Detection via Scene-Specific Fusion Modules](https://mlsanity.com/paper/2310.19372/) (2023)
- [MonoTher-Depth: Enhancing Thermal Depth Estimation via Confidence-Aware Distillation](https://mlsanity.com/paper/2504.16127/) (2025)
- [AnyThermal: Towards Learning Universal Representations for Thermal Perception](https://mlsanity.com/paper/2602.06203/) (2026)
- [Vision-Based Detection of Uncooperative Targets and Components on Small Satellites](https://mlsanity.com/paper/2408.12084/) (2024)
- [Semantics from Space: Satellite-Guided Thermal Semantic Segmentation Annotation for Aerial Field Robots](https://mlsanity.com/paper/2403.14056/) (2024)

## Abstract (arXiv)

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.