With the development of remote sensing technology, high-resolution urban remote sensing images contain more structural and textural information of buildings. Buildings in urban mostly have clear corners and homogeneous roofregions. However, the instability of imaging conditions usually causes blur, light changes and other affine transformations to remote sensing images. Combined with Hessian-Affine and MSER, a fused affine region detection algorithm is proposed. Regions highly covered by others are selected according to the overlap error. Then these selected regions are considered whether to be deleted according to the affine match score. The building images’ average repeatability and number of correspondence are used for evaluation and analysis on detection. Experiments results show that the proposed method make full use of the two complementary detectors, and it obtains the best average repeatability, less redundancy under the different types of transformations. Therefore, the proposed method is better for urban remote sensing application fields.