Regional Agricultural Land Texture Classification Based on GLCMs, SVM and Decision Tree Induction Techniques

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.