Cloud Classification Based on Images Texture Features

Abstract

Abstract An identification of cloud imagery is part of the cloud observation process which is very important to know the potential for weather changes, especially in the Sultan Hasanuddin airport area. The purpose of this research is to build an artificial intelligence model to identify and classify texture patterns of cloud images. The research used 80 clouds images data contained in the Sultan Hasanuddin Airport area. The data consist of four types of clouds, Altocumulus, Cirrus, Cumulonimbus and Cumulus. In this research, a feature extraction process using Gray Level Co-occurrence Matrix (GLCM) algorithm and Support Vector Machine (SVM) is used for the classification process. We used a set of 4 GLCM features. The 4 selected features are contrast, correlation, energy and homogeneity. Training and testing data using cross validation method with three stages validation. The highest level of accuracy is found in the third stage validation with an accuracy value of 85%.

Access to Document

Other files and links

• Link to publication in Scopus
• Open Access Version Available

Fingerprint