**4. Conclusions**

In this study, a robust synthetic aperture radar (SAR) automatic target recognition approach was presented that applied sparse representation to produce a weighted global feature vector based on spatial pyramid matching. SAR image target recognition was performed on the reconstructed image representation. The experimental results clearly and consistently showed that the proposed framework was significantly more discriminative than the traditional SPM methods. Moreover, the feature coding–pooling framework was shown to perform well in image classification tasks, the unavoidable information loss incurred by the feature quantization in the coding process and the undesired dependence of pooling on the image spatial layout, however, may severely limit the recognition performance. Therefore, more systematic investigations are still required to find a new coding method to improve the classification performance in future research.

**Author Contributions:** Conceptualization, S.W. and Y.L.; methodology, S.W.; software, Y.L.; validation, L.L.; formal analysis, S.W.; investigation, L.L.; resources, S.W.; data curation, S.W.; writing original draft preparation, Y.L.; writing—review and editing, S.W.; visualization, L.L.; supervision, S.W.; project administration, S.W.; funding acquisition, S.W. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded in part by the National Natural Science Foundation of China (Grant No. 61901297) and in part by the National Science Foundation of Tianjin Province of China, (Grant No. 18JCQNJC70600).

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Conflicts of Interest:** The authors declare no conflict of interest.
