# High-Dynamic-Range Imaging for Cloud Segmentation

arXiv: https://arxiv.org/abs/1803.01071 · HTML: https://mlsanity.com/paper/1803.01071/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Mar 2018.

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

- [Using U-Net Network for Efficient Brain Tumor Segmentation in MRI Images](https://mlsanity.com/paper/2211.01885/) (2022)
- [Open-Source Ground-based Sky Image Datasets for Very Short-term Solar Forecasting, Cloud Analysis and Modeling: A Comprehensive Survey](https://mlsanity.com/paper/2211.14709/) (2022)
- [Enhancing coastal water body segmentation with Landsat Irish Coastal Segmentation (LICS) dataset](https://mlsanity.com/paper/2409.15311/) (2024)
- [Towards Physically-Based Sky-Modeling](https://mlsanity.com/paper/2412.11883/) (2024)
- [Segregation and Context Aggregation Network for Real-time Cloud Segmentation](https://mlsanity.com/paper/2504.14178/) (2025)
- [Towards Physically-Based Sky-Modeling For Image Based Lighting](https://mlsanity.com/paper/2512.15632/) (2025)

## Abstract (arXiv)

Sky/cloud images obtained from ground-based sky-cameras are usually captured using a fish-eye lens with a wide field of view. However, the sky exhibits a large dynamic range in terms of luminance, more than a conventional camera can capture. It is thus difficult to capture the details of an entire scene with a regular camera in a single shot. In most cases, the circumsolar region is over-exposed, and the regions near the horizon are under-exposed. This renders cloud segmentation for such images difficult. In this paper, we propose HDRCloudSeg -- an effective method for cloud segmentation using High-Dynamic-Range (HDR) imaging based on multi-exposure fusion. We describe the HDR image generation process and release a new database to the community for benchmarking. Our proposed approach is the first using HDR radiance maps for cloud segmentation and achieves very good results.