This study proposed a new method of urban impervious surface extraction from very high resolution (VHR) imagery, by combining spatial and spectral unmixing and decision tree classification. Endmember classes were first defined, where two shadow endmembers were used. Spatial homogeneity and spectral purity were fused to extract endmembers, i.e., spatial and spectral endmember. A modified multiple endmember unmixing method was adopted to unmix VHR image to produce endmember fraction images. Decision rules were then created to produce final impervious surface extraction results. The experimental results using Worldview-2 multispectral image of Beijing urban area validates the proposed method, by comparing with existing methods.