A practical method is proposed in this paper for building extraction from remote sensing images with

high spatial resolution. Relevant features between building and its neighboring shade were used to establish the

method. The steps of the approach were as follows: First，the high-resolution merged image was constructed, which

combined the Grey Level Concurrence Matrix （GLCM） homogeneity texture feature and the normalized difference

vegetation index (NDVI) segmented by arithmetic of multi-resolution segments and two scale feature unit layers.

Second, water and land were separated by a threshold of normalized difference water indices (NDWI) based on the

larger scale object and then the underlying building region was extracted by the decision rule based on spectral

and shape features of the object from the land region according to the larger scale object layer. Third, shade was

extracted by the knowledge rule based on the mean value of the near infrared band of objects in the small-scale

objects layer. After that, the class-related feature neighboring the shade was defined. Finally, a building was

extracted from the building region by searching feature unit objects neighboring the shade. The experimental

result based on the QuickBird image shows that the proposed method is very effective and suitable for building

extraction from high spatial resolution remote images.