Open AccessCloud Classification Based on Structure Features of Infrared Images
Author(s) -
Lei Liu,
Xiangyi Sun,
Feng Chen,
Shuang Zhao,
Taichang Gao
Publication year - 2011
Publication title -
journal of atmospheric and oceanic technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.774
H-Index - 124
eISSN - 1520-0426
pISSN - 0739-0572
DOI - 10.1175/2010jtecha1385.1
Subject(s) - cloud computing , computer science , artificial intelligence , classifier (uml) , sky , segmentation , image segmentation , infrared , pattern recognition (psychology) , feature extraction , computer vision , remote sensing , physics , geology , meteorology , optics , operating system
Some cloud structure features that can be extracted from infrared images of the sky are suggested for cloud classification. Both the features and the classifier are developed over zenithal images taken by the whole-sky infrared cloud-measuring system (WSIRCMS), which is placed in Nanjing, China. Before feature extraction, the original infrared image was smoothed to suppress noise. Then, the image was enhanced using top-hat transformation and a high-pass filtering. Edges are detected from the enhanced image after adaptive optimization threshold segmentation and morphological edge detection. Several structural features are extracted from the segment image and edge image, such as cloud gray mean value (ME), cloud fraction (ECF), edge sharpness (ES), and cloud mass and gap distribution parameters, including very small-sized cloud mass and gaps (SMG), middle-sized cloud gaps (MG), medium–small-sized cloud gaps (MSG), and main cloud mass (MM). It is found that these features are useful for distinguishing cirriform, cumuliform, and waveform clouds. A simple but efficient supervised classifier called the rectangle method is used to do cloud classification. The performance of the classifier is assessed with an a priori classification carried out by visual inspection of 277 images. The index of agreement is 90.97%.