Infrared imaging is an important measure for night observation, which is widely used in both military and civil fields. For infrared imagery target classification problem, the monogenic signal was introduced for feature extraction, which was used to analyze the target characteristics. The infrared image after single signal processing can be described by the amplitude, phase and orientation components. For each component, its multi-scale decompositions were processed by connection and downsampling to achieve one single feature vector. Finally, three feature vectors were generated to describe the multi-layer properties of the target. The joint sparse representation was employed as the representation model for the three feature vectors, which used their correlation to improve the overall reconstruction precision. The reconstruction errors of different classes were calculated based on the results from joint sparse representation and the target label could be further decided. The experiments were conducted on the medium wave infrared (MWIR) image dataset to classify the original, noisy, and occluded samples. By comparison with several existed algorithms, the validity and robustness of the proposed method for infrared imagery target classification could be confirmed.