Paper Info
Title
Regional Agricultural Land Texture Classification Based on GLCMs, SVM and Decision Tree Induction Techniques
Abstract
Texture is a natural characteristic of all surfaces, which describes the visual patterns, and each has homogenization properties. In this paper, our concern is with the Regional Agricultural Land texture classification using grey level co-occurrence matrices (GLCMs). Firstly, the Gabor filter is applied to the test image in order to allow a certain band of frequencies and reject the others. Then texture discrimination is performed to partition a textured image into areas, each related to a homogeneous texture where samples of four different textures are extracted from the image. For each patch, the four features of the GLCM matrices namely, dissimilarity, correlation, angular second moment, and homogeneity are computed. Finally, for texture classification we have used two well-known methods: Support Vector Machine and decision tree induction. The results show that these texture features have high discrimination accuracy and classification using support vector machines gives better results as compared to the decision tree induction classifier.
Year
DOI
Venue
2018
10.1109/CEEC.2018.8674193
2018 10th Computer Science and Electronic Engineering (CEEC)
Keywords
Field
DocType
Feature extraction,Decision trees,Support vector machines,Correlation,Surface texture,Wavelet transforms,Optical variables measurement
Decision tree,Homogeneity (statistics),Pattern recognition,Computer science,Support vector machine,Gabor filter,Feature extraction,Artificial intelligence,Classifier (linguistics),Standard test image,Wavelet transform
Conference
ISSN
ISBN
Citations 
2472-1530
978-1-5386-7275-4
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Nassr Azeez100.34
Inas Al-Taie200.34
Wafa Yahya300.34
Arwa Basbrain400.34
Adrian F. Clark522172.99