This paper addresses the automatic interpretation of digital image of three-dimensional scenes, especially automatic recognition of three-dimensional aircraft types from digital images. First, an efficient coordinate transform from a series of two-dimensional aircraft posture silhouette images to invariant matrices is developed. The invariant matrix is independent of its translation, scaling, and rotation. Next, on the basis of the invariant matrix, an effective algebraic feature extraction method is proposed. The method is based on singular value decomposition (SVD) of the matrix. To compress the dimensionality of the singular value vector, an optimal discriminant transform for a small number of samples is introduced to transform an original feature space of singular value vector into a new feature space in which its dimensionality is very low. Finally, our method is used to recognize three-dimensional aircraft types Experimental results show that our algebraic method as a high recognition rate, and it is insensitive to translation, scaling, rotation, and noise.