The effect of scale is continuously attracting attentions in geomatics

bionomics and environmentology.Many methods have been developed for the selection of optimal scale

including those based on local variance

variogram and transformed divergence.However

there are some problems associated with these methods

which limit their applications in practice.This paper presents a new method for optimal scale selection

based on information entropy.The novelty of this new method is that the multi-spectral information is fully used to define the optimal scale.In this method

（a） information entropy is introduced to quantify the uncertainty in image classification;（b） the spatial distribution is also taken into account.This new method has been evaluated and also compared with the existing methods

i.e.

those based on local variance

variogram and transformed divergence.Two types of image are used

i.e.TM（Thematic Mapper） which has relatively low resolution and Quickbird image which has high resolution.The experimental results show that the proposed algorithm is capable of effectively determining the optimal scale for these images.In the case of classification of Quickbird image

objected-oriented classification technology is used and the results prove that the new method not only works well with traditional classifiers but also performs with object-oriented classifiers for high resolution images.A comparative analysis shows that the new method performs much better than existing methods.