On the basis of a case study in Miyun area of Beijing, a new strategy of classification of land-

use/cover integrated with the up-scaling methods and object multi-features in the high resolution SPOT fused image

was introduced. Multi-resolution dataset was built using up-scaling methods, and optimal resolution images were

selected by semi-variance analysis. Relevant optimal spatial resolution images were adopted for different classes.

Object multi-features, which included spectral information, generic shape features, class related features, and

new computed features, were introduced. A multi-scale decision tree was set up based on object multi-features, and

different classes were extracted from multi-resolution images. Afterwards, further discussion and comparison for

improving the efficiency and accuracy of classification were presented. The results show that the proposed image

analysis approach can successfully decrease the heterogeneity, smooth the noise influence, reduce computational

and storage burdens and improve the classification efficiency in the high spatial resolution image.