Open AccessLand cover classification using Grey Level Co-occurrence Matrix and Naive Bayes
Author(s) -
Sofia Saidah,
Nor Kumalasari Caecar Pratiwi,
Bandiyah Sri Aprilia,
Rita Magdalena,
Yunendah Nur Fuadah
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1367/1/012073
Subject(s) - naive bayes classifier , land cover , cover (algebra) , class (philosophy) , computer science , pixel , field (mathematics) , contextual image classification , bayes' theorem , pattern recognition (psychology) , land use , artificial intelligence , process (computing) , data mining , remote sensing , geography , image (mathematics) , mathematics , bayesian probability , engineering , support vector machine , mechanical engineering , civil engineering , pure mathematics , operating system
Land cover data is important information to describe how much of a region is covered by plantation, forest, residential, rice field and river. In many applications the required information relates to the coverage of land cover class in a region, which is generally derived from a count of the pixels allocated to the class of interest in a classification. The design of the system in this study conducted for detecting land cover using Grey Level Co Occurrence (GLCM) method is used as the extraction in process of taking main image and Naive Bayes as a classification of grouping the images based on the types of land cover. Based on the testing data which is consist of 150 images we obtained the best accuracy is 85% with 206.6715 seconds computation time.