Aerial Image Classification Method Based on Fractal Theory
基于分形理论的航空图像分类方法

Remote sensing images have both spectral and textural features. How to make uses of these features is very important to the practical work of remote sensing image classification. This paper presents a supervised classification method of aerial remote sensing image, which takes advantages of both spectral features and textural features. First, this paper puts forward a set of textural features with their computation approaches based on fractal and multifractal theory, including fractal dimension, multifractal function %q_D(q)%, and lacunarity. The fractal_based textural features are relatively insensitive to the image scaling, therefore, within certain scope, the fractal_based textural features obtained from a remote sensing image under one resolution can also be used in the remote sensing images under other resolutions. This is very valuable in practice. Then, this paper presents the classification method which consists of two parts, namely feature extraction and classifier construction. In the part of feature extraction, this method converts color aerial image from RGB to HSI and computes fractal dimension, multifractal function %q_D(q)%, and lacunarity by intensity as texture features with normalized hue and saturation being used as spectral features. In the part of classifier construction, it adopts BP neuval network as classifier. In the end, the experiment of classifying the aerial images has been done and the result is satisfactory, which verifies the effect of this methood.