Improving Texture Based Classification of Aerial Images by Fractal Features

In this paper we propose an effective method of aerial image classification, which combines three types of features: color-based, statistical and fractal information. Two distinct phases were necessary for the CBIR system, which includes the classification algorithm: the learning phase and the classification phase. In the learning phase 5 different and efficient features were selected: entropy, contrast, homogeneity, mass fractal dimension and lacunarity. Also, three categories (classes) in CBIR were considered. The method of comparison, based on sub-images, improves the texture-based classification. A set of 100 aerial images from UAV was tested for establishing the rate of classification. The rate of 96% accurate classification, obtained as result, confirms the efficiency of the proposed method.