ing at the specific application of building detection, a sparse representation-based full-reference quality assessment method for distorted satellite stereoscopic images is proposed. First, a new distorted satellite stereo image database is constructed, in which the corner detection and the digital surface model elevation information are used for building detection. And a detection accuracy index is proposed to represent the degree of distortion based on the change of the detected corners. Then, an objective evaluation model based on sparse representation is proposed, which extracts scale-invariant feature transforms and binary robust invariant scalable key points of the original and the distorted images for dictionary learning. Four quality scores are obtained using sparse representation to measure the similarity between the original and the distorted images. Finally, the final objective assessment value is obtained by fusing the four quality scores using support vector regression. The test is carried out on the constructed database. The test results on the constructed database show that the Pearson linear correlation coefficient is higher than 0.90, and the Spearman rank correlation coefficient is higher than 0.87. Compared with the existing assessment methods, the proposed objective evaluation method can better reflect the quality of satellite stereo images.