In this paper, an airplane detection algorithm based on sparse representation is proposed to solve the problems of low detection precision in complicated backgrounds and difficult achivement of rotation invariant.Three steps are included in this algorithm:first, a geometrical dictionary is built according to the rigid property and the typical geometric appearance of the airplane to be detected;second, the optimal combination of the geometrical parts is obtained by solving the airplane profile approximation model set up under the framework of sparse representation theory;third, the object salient map is generated based on the star-structure part-based model and the location of the objects can be obtained from the salient map.Experimental results indicate that this algorithm can adaptively select the geometrical parts of airplane as few as possible and is insusceptible to illumination, color or complicated backgrounds.Compared to existing methods, the detection precision of our algorithm reaches above 90%, and the detection speed is significantly improved.