Open AccessComparative analysis of classification techniques for leaves and land cover texture
Author(s) -
Azri Azrul Azmer,
Norlida Hassan,
Shihab Hamad Khaleefah,
Salama A. Mostafa,
Azizul Azhar Ramli
Publication year - 2021
Publication title -
ijain (international journal of advances in intelligent informatics)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.183
H-Index - 9
eISSN - 2548-3161
pISSN - 2442-6571
DOI - 10.26555/ijain.v7i3.706
Subject(s) - texture (cosmology) , pattern recognition (psychology) , cover (algebra) , naive bayes classifier , land cover , random forest , artificial intelligence , computer science , object (grammar) , k nearest neighbors algorithm , image (mathematics) , land use , support vector machine , engineering , mechanical engineering , civil engineering
The texture is the object’s appearance with different surfaces and sizes. It is mainly helpful for different applications, including object recognition, fingerprinting, and surface analysis. The goal of this research is to investigate the best classification models among the Naive Bayes (NB), Random Forest (DF), and k-Nearest Neighbor (k-NN) algorithms in performing texture classification. The algorithms classify the leaves and urban land cover of texture using several evaluation criteria. This research project aims to prove that the accuracy can be used on data of texture that have turned in a group of different types of data target based on the texture’s characteristic and find out which classification algorithm has better performance when analyzing texture patterns. The test results show that the NB algorithm has the best overall accuracy of 78.67% for the leaves dataset and 93.60% overall accuracy for the urban land cover dataset.