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

TL;DR

This paper systematically evaluates various color spaces and components to improve the segmentation of sky and cloud images, using PCA and fuzzy clustering to identify the most effective features.

Contribution

It introduces a systematic method for selecting optimal color components for sky/cloud image segmentation, enhancing accuracy over previous ad hoc approaches.

Findings

Identifies key color components for sky/cloud segmentation.

Demonstrates the effectiveness of PCA and fuzzy clustering in feature selection.

Provides guidelines for choosing color spaces in cloud imaging applications.

Abstract

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.