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

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.

Interpreting the Fuzzy Semantics of Natural-Language Spatial Relation Terms with the Fuzzy Random Forest Algorithm

“…Indeed, it has been proven to be useful at handling non-spatial information, spatial objects, formalized relations and remote sensing images [15,16]. Wang et al [17] and Wang [18] developed fuzzy models to improve the representation, analysis, and query of non-spatial information in natural languages, such as low humidity and high elevation.…”

High-Dynamic-Range Imaging for Cloud Segmentation

“…In our previous work (Dev et al, 2014a), we have provided a detailed study of various color channels and models, that are conducive for sky/cloud image segmentation. It provides a detailed analysis of the different color channels and components, 25 and also explains why certain ratio color channels are better for cloud segmentation.…”

“…While there have been several studies analyzing clouds and their features from WSI images (Long et al, 2006;Souza-Echer et al, 2006;Li et al, 2011;Liu et al, 2015a;Dev et al, 2014a), most of them avoid the circumsolar region, because capturing the details in this area is a non-trivial task. On a typical sunny and clear day, it is difficult to capture the entire luminance range of the sky scene using a conventional camera.…”

“…In our previous work (Dev et al, 2014a), we have performed a systematic analysis of the existing color spaces and components for conventional LDR images, and observed that color channels, such as (B − R)/(B + R) is one of the most discriminating color channels for cloud segmentation (B and R indicate the blue and red channels of the image). We discuss further details on discriminatory color channels of HDR 25 luminance maps in Section 4.2.1.…”

“…We discuss further details on discriminatory color channels of HDR 25 luminance maps in Section 4.2.1. Furthermore, in Dev et al (2014a), we have proposed a probabilistic approach for sky/cloud image segmentation, instead of the conventional binary approach. In this probabilistic approach, each pixel is assigned a soft membership to belong to cloud category, instead of a hard membership.…”