# Systematic study of color spaces and components for the segmentation of sky/cloud images

arXiv: https://arxiv.org/abs/1701.04520 · HTML: https://mlsanity.com/paper/1701.04520/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jan 2017.

## Built on by (most cited first, 9 of 9)

- [Using GANs to Augment Data for Cloud Image Segmentation Task](https://mlsanity.com/paper/2106.03064/) (2021)
- [DDUNet: Dual Dynamic U-Net for Highly-Efficient Cloud Segmentation](https://mlsanity.com/paper/2501.15385/) (2025)
- [UCloudNet: A Residual U-Net with Deep Supervision for Cloud Image Segmentation](https://mlsanity.com/paper/2501.06440/) (2025)
- [Using U-Net Network for Efficient Brain Tumor Segmentation in MRI Images](https://mlsanity.com/paper/2211.01885/) (2022)
- [Supervised segmentation of NO2 plumes from individual ships using TROPOMI satellite data](https://mlsanity.com/paper/2203.06993/) (2022)
- [ACLNet: An Attention and Clustering-based Cloud Segmentation Network](https://mlsanity.com/paper/2207.06277/) (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)
- [Detecting Blurred Ground-based Sky/Cloud Images](https://mlsanity.com/paper/2110.09764/) (2021)
- [Segregation and Context Aggregation Network for Real-time Cloud Segmentation](https://mlsanity.com/paper/2504.14178/) (2025)

## Abstract (arXiv)

Sky/cloud imaging using ground-based Whole Sky Imagers (WSI) is a cost-effective means to understanding cloud cover and weather patterns. The accurate segmentation of clouds in these images is a challenging task, as clouds do not possess any clear structure. Several algorithms using different color models have been proposed in the literature. This paper presents a systematic approach for the selection of color spaces and components for optimal segmentation of sky/cloud images. Using mainly principal component analysis (PCA) and fuzzy clustering for evaluation, we identify the most suitable color components for this task.