代表性国外著名期刊论文：

1. Xin Hu, Yan Wu, Xingyu Liu, Zhikang Li , Zhifei Yang and Ming Li. “Intra- and Inter-Modal Graph Attention Network  and Contrastive Learning for SAR and Optical Image Registration”.  IEEE Transactions on Geoscience and Remote Sensing, vol.61, pp.5220216, 2023.
2. Y. Cao, Y. Wu, M. Li, M. Zheng, P. Zhang, and J. Wang, “Multifrequency PolSAR Image Fusion Classification Based on Semantic Interactive Information and Topological Structure,” IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp.5205715, 2023.
3. X. Xin, M. Li, Y. Wu, M. Zheng, P. Zhang, D. Xu, and J. Wang, “Semi-Supervised Classification of Dual-Frequency PolSAR Image Using Joint Feature Learning and Cross Label-Information Network,” IEEE Transactions on Geoscience and Remote Sensing. vol. 60, pp.5235716, 2022.
4. W. Liang, Y. Wu, M. Li, and Y. Cao, “A Feature Fusion-Net Using Deep Spatial Context Encoder and Nonstationary Joint Statistical Model for High-Resolution SAR Image Classification,” IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 4407818, 2022.
5. Y. Cao, Y. Wu, M. Li, W. Liang, and X. Hu, “DFAF-Net: A Dual-Frequency PolSAR Image Classification Network Based on Frequency-Aware Attention and Adaptive Feature Fusion,” IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 5224318, 2022.
6. W. Liang, Y. Wu, M. Li, Y. Cao. High Resolution SAR Image Classification Using Context-Aware Encoder Network and Hybrid Conditional Random Field Model. IEEE Trans on Geoscience and Remote Sensing. August, 2020,58(8):5317-5335.
7. Z. Yang, Y. Wu, M. Li, X. Hu, Z. Li, Unsupervised change detection in PolSAR images using siamese encoder–decoder framework based on graph-context attention network. International Journal of Applied Earth Observation and Geoinformation. 2023, 124, 103511.
8. Y. Cao, Y. Wu, P. Zhang, W. Liang, and M. Li. Pixel-Wise PolSAR Image Classification via a Novel Complex-Valued Deep Fully Convolutional Network. Remote Sensing. 2019, 11(22): 2653-2682. 
9. J.W. Fan, Y. Wu, M. Li, W.K. Liang, and Y.C. Cao. SAR and Optical Image Registration Using Nonlinear Diffusion and Phase Congruency Structural Descriptor. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(9):5368-5379.
10. W.Y. Song, M. Li, P. Zhang, Y.Wu. Fuzziness modeling of polarized scattering mechanisms and PolSAR image classification using fuzzy triplet discriminative random fields [J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(7): 4980-4993.
11. W.Y. Song, M. Li, P. Zhang, Y. Wu. Superpixel-based hybrid discriminative random field for fast PolSAR image classification [J]. IEEE Access, 2019, 7: 24547-24558.
12. W.Y Song , M. Li,  P. Zhang , Y. Wu , X.F. Tan, and L.An Mixture WG_-MRF Model for PolSAR Image Classification. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(2): 905-920.
13. F. Wang, YanWu, Ming Li. Adaptive hybrid conditional random fields model for SAR image segmentation.IEEE Trans on Geoscience and Remote Sensing,2017,55(1):537-550.
14. J.W.Fan,Yan Wu,F.Wang.New Point Matching Algorithm Using Sparse Representation of Image Patch Feature for SAR Image Registration.IEEE Trans on Geoscience and Sensing,2017,55(3):1498-1510.
15. F. Wang, Yan Wu, Ming Li.Unsupervised SAR Image Segmentation Using Higher Order Neighborhood-Based Triplet Markov Fields Model. IEEE Trans on Geoscience and Remote Sensing, Vol.52, No.8, August 2014, pp.5193–5205.
16. Yan Wu,P. Zhang ,M.Li. SAR Image Multiclass Segmentation Using a Multiscale and Multidirection TMF Model in NSCT Domain. Imformation Fusion. 2013（14）441-449
17. Yan Wu, Fan Wang, Qingjun Zhang, Fanglong Niu, Ming Li."Fast algorithm based on superpixel-level conditional triplet Markov field for successive approximation resistor image segmentation ", IET Radar, Sonar and Navigation, 2015, Vol. 9, Iss. 8, pp. 1097–1105.
18. P.Zhang,M.Li,Yan Wu. Unsupervised Multi-class Segmentation of SAR images using Fuzzy Triplet Markov Fields Model. Pattern Recognition. 2012, 45(11): 4018-4033 .
19. J.W.Fan,YanWu,Ming.Li.SAR Image Registration Using Multiscale Image Features with Sparse Representation.IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2017,10(4):1483-1493
20. W.Y Song , M. Li,  P. Zhang , Y. Wu , L.Jia, and L.An. Unsupervised PolSAR Image Classification and Segmentation Using Dirichlet Process Mixture Model and Markov Random Fields With Similarity Measure. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017, 10(8): 3556-3568.
21. Peng Zhang, Ming Li, Yan Wu，Li Hejing. Hierarchical Conditional Random Fields Model for Semisupervised SAR Image Segmentation. IEEE Trans on Geoscience and Remote Sensing, Vol 53, No 9, September , 2015, pp 4933-4951
22. L. Jia, M. Li, P. Zhang, and Y. Wu. SAR image change detection based on correlation kernel and multistage extreme learning machine [J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(10): 5993-6006.
23. L. Jia,M.Li, Yan Wu. SAR Image Change Detection based on iterative label-information composite kernel supervised by anisotropic texture. IEEE Trans on Geoscience and Remote Sensing.2015,53(7):3960-3973.
24. Yan.Wu, M.Li,P.Zhang, H.Zong, P.Xiao,C.Liu. Unsupervised multi-class segmentation of SAR images using triplet Markov fields models based on edge penalty. Pattern Recognition Letters. 2011, 32（11）: 1532-1540 .
25. M.Liu, Yan Wu, Q.Zhang, M. Li.“Dempster–Shafer fusion of multiple sparse representation and statistical property for SAR target configuration recognition,” IEEE Geosci. Remote Sens. Lett., vol. 11, no. 6, pp. 1106–1110, Jun. 2014.
26. X.J.Lian, Yan Wu, F.Wang, Q.Zhang, and M.Li, “Unsupervised SAR image segmentation based on conditional Triplet Markov fields,” IEEE Geosci. Remote Sens. Lett., vol. 11, no. 7, pp. 1185–1189, July 2014.
27. Lu Gan, Yan Wu, Fan Wang, Peng Zhang, and Qiang Zhang.Unsupervised SAR Image Segmentation Based on Triplet Markov Fields with Graph Cuts. IEEE Geoscience and Remote Sensing Letters, Vol.11, No.4, April, 2014, pp.853–857.
28. Q. Zhang, Yan Wu, W. Zhao, F. Wang, J. W. Fan, and M. Li, “Multiple-scale salient-region Detection of SAR image based on Gamma distribution and local intensity variation,” IEEE Geosci. Remote Sens. Lett., vol. 11, no. 8, pp. 1370–1374, Aug. 2014.
29. L.Jia, M.Li, Yan Wu, P. Zhang, H.Chen, L. An. Semisupervised SAR Image Change Detection Using a Cluster-Neighborhood Kernel. IEEE Geosci. Remote Sens. Lett., vol.11, no.8, pp. 1443-1447, Aug.2014.
30. Ming Liu, Yan Wu, Peng Zhang, Qiang Zhang, Yanxin Li, and Ming Li. "SAR Target Configuration Recognition Using Locality Preserving Property and Gaussian Mixture Distribution". IEEE Geosci. Remote Sens. Lett., vol. 10, no. 2, pp. 268-272, Mar. 2013.
31. F. Wang, Yan Wu, Q. Zhang, P. Zhang, M. Li, and Y. Lu."Unsupervised Change Detection on SAR Images Using Triplet Markov Field Model". IEEE Geosci. Remote Sens. Lett., 10(4). 697-701, Jul. 2013.
32. P. Zhang, M. Li,Yan Wu. Unsupervised SAR Image Segmentation Using a Hierarchical TMF Model. IEEE Geosci. Remote Sens. Lett., vol. 10, no.5, pp.971-975, 2013.
33. L.Gan, Yan Wu, M. Liu, P. Zhang, H. Ji, F. Wang. Triplet Markov Fields with Edge Location for Fast Unsupervised Multi-class Segmentation of SAR Images. IET Image Process., 2012, 6(7), pp. 831–838.
34. P. Zhang , M. Li, Yan Wu.An Improved Particle Filter Algorithm Based on Markov Random Field Modeling in Stationary Wavelet Domain for SAR Image Despeckling. Pattern Recognition Letters. 2012, 33(10): 1316–1328.
35. P. Zhang, M. Li,Yan Wu, M. Liu, F. Wang, and L. Gan. SAR image multiclass segmentation using a multiscale TMF model in Wavelet Domain. IEEE Geosci. Remote Sens. Lett., vol. 9, no.6, pp.1099-1103, 2012.
36. Yan Wu, X. Wang, P. Xiao, L. Gan, C. Liu, M. Li. “Fast algorithm based on triplet Markov fields for unsupervised multi-class segmentation of SAR images”. Science in China Series F :Imformation Sciences. 2011, 54(7): 1524–1533.