Land cover classification using Grey Level Co-occurrence Matrix and Naive Bayes

Abstract

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.