提出一种基于分形理论和BP神经网络的航空遥感图像有监督分类方法。该方法尝试将航空图像的光谱信息和纹理特征相结合。它首先将彩色航空图像由RGB格式转化为HSI格式，然后，根据亮度计算分数维、多重分形广义维数谱q-D(q)和“空隙”等基于分形的纹理特征，同时加入归一化的色度和饱和度作为光谱特征，采用BP神经网络作为分类器。通过对彩色航空图像的分类实验，结果证实该方法行之有效。

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.