# Nighttime sky/cloud image segmentation

## TL;DR

This paper introduces a superpixel-based segmentation method for nighttime sky/cloud images, addressing the challenges of low light and noise, and provides the first dedicated dataset for this task.

## Contribution

The paper presents a novel superpixel-based segmentation algorithm specifically designed for nighttime sky/cloud images and releases the first dataset for this purpose.

## Key findings

- The proposed method effectively segments nighttime sky/cloud images.
- The new dataset facilitates further research in nighttime atmospheric imaging.
- Experimental results demonstrate the algorithm's robustness under low-light conditions.

## Abstract

Imaging the atmosphere using ground-based sky cameras is a popular approach to study various atmospheric phenomena. However, it usually focuses on the daytime. Nighttime sky/cloud images are darker and noisier, and thus harder to analyze. An accurate segmentation of sky/cloud images is already challenging because of the clouds' non-rigid structure and size, and the lower and less stable illumination of the night sky increases the difficulty. Nonetheless, nighttime cloud imaging is essential in certain applications, such as continuous weather analysis and satellite communication. In this paper, we propose a superpixel-based method to segment nighttime sky/cloud images. We also release the first nighttime sky/cloud image segmentation database to the research community. The experimental results show the efficacy of our proposed algorithm for nighttime images.