The distribution of urban impervious surface is a significant indicator of the degree of urbanization, as well as a major indicator of environmental quality. Hence, taking advantage of remotely sensed imagery to map impervious surface has become an important topic. Spectral indices have been developed due to its convenience to apply, among which feature extraction approach has shown superiority in reliability and applicability. However, impervious surface is often confused with bare soil when the current existing indices are used as well as their sensor-specific limitations. In this study, a new index, combinational build-up index (CBI), is proposed to extract impervious surface. The new index combines the first component of a principal component analysis (PC1), normalized difference water index (NDWI), and soil-adjusted vegetation index (SAVI), representing high albedo, low albedo, and vegetation, respectively, to reduce the original bands into three thematic-oriented features. The new index was tested using various remote sensing images at different spectral and spatial resolutions. Qualitative and quantitative assessments of the accuracy and separability of CBI, together with the comparison with other existing indices, were performed. The result of this study indicates that the proposed method is able to serve as an effective impervious index and can be applied widely.