SYSTEMATIC STUDY OF COLOR SPACES AND COMPONENTS FOR THE SEGMENTATION OF SKY/CLOUD IMAGES

被引：0

作者：

Dev, Soumyabrata ^{[1
]}

Lee, Yee Hui ^{[1
]}

Winkler, Stefan ^{[2
]}

机构：

[1] Nanyang Technol Univ, Sch Elect & Elect Engn, Singapore 639798, Singapore

[2] Univ Illinois, ADSC, Singapore 138632, Singapore

来源：

2014 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP) | 2014年

关键词：

Remote sensing; Image segmentation; Color spaces; Principal Component Analysis; Clustering; SKY IMAGES; CLOUD; CLASSIFICATION; ALGORITHM;

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

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.

引用

页码：5102 / 5106

页数：5

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