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

b

ased textural features are relatively insensitive to the image scaling

therefore

within certain scope

the fractal

b

ased 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.