近期论文
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期刊论文(第一作者、通讯作者(*)):
[1] Z. Liu, L. Ma, and Q. Du, “Class-wise distribution adaptation for unsupervised classification of hyperspectral remote sensing images,” IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 1, pp. 508–521, January 2021. (SCI, T2) IF=5.855
[2] W. Wang, L. Ma, M. Chen, and Q. Du, “Joint correlation alignment based graph neural network for domain adaptation of multitemporal hyperspectral remote sensing images,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021, DOI: 10.1109/JSTARS. 2021.3063460. (SCI, T2) IF=3.827
[3] H. Wei, L. Ma, Y. Liu, and Q. Du, “Combining multiple classifiers for domain adaptation of remote sensing image classification,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 14, pp. 1832–1847, January 2021. (SCI, T2) IF=3.827
[4] M. Chen, L. Ma, W. Wang, and Q. Du, “Augmented associative learning-based domain adaptation for classification of hyperspectral remote sensing images,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 13, pp. 6236–6248, October 2020. (SCI, T2) IF=3.827
[5] L. Ma, M. M. Crawford, L. Zhu and Y. Liu, “Centroid and covariance alignment-based domain adaptation for unsupervised classification of remote sensing images,” IEEE Transactions on Geoscience and Remote Sensing, vol. 57, no. 4, pp. 2305–2323, April 2019. (SCI, T2) IF=5.855(Web of Science引用次)
[6] L. Ma, C. Luo, J. Peng and Q. Du, “Unsupervised manifold alignment for cross-domain classification of remote sensing images,” IEEE Geoscience and Remote Sensing Letters, vol. 16, no. 10, pp. 1650–1654, October 2019. (SCI, T3) IF=3.833
[7] L. Zhou and L. Ma, “Extreme learning machine-based heterogeneous domain adaptation for classification of hyperspectral images,” IEEE Geoscience and Remote Sensing Letters, vol. 16, no. 11, pp. 1781–1785, November 2019. (SCI, T3) IF=3.833
[8] C. Luo and L. Ma, “Manifold regularized distribution adaptation for classification of remote sensing images,” IEEE Access, vol. 6, no. 1, pp. 4697-4708, 2018. (SCI, T3) IF=3.745
[9] Li Ma, Jiazhen Song, Deep neural network-based domain adaptation for classification of remote sensing images, Journal of Applied Remote Sensing, 2017, 11(4), 042612. (SCI, T4)
[10] Li Ma, Xiaofeng Zhang, Xin Yu, Dapeng Luo, Spatial Regularized Local Manifold Learning for Classification of Hyperspectral Images, IEEE Journal of Selected Topics in Applied Earth Observation and Remote Sensing, 2016, 9(2): 609- 624. (SCI, T2) IF=3.827(Web of Science引用次)
[11] L. Ma, A. Ma, C. Ju, and X. Li, "Graph-based semi-supervised learning for spectral-spatial hyperspectral image classification," Pattern Recognition Letters, vol. 83, pp. 133-142, 2016. (SCI, T3) IF=3.255(Web of Science引用)
[12] L. Zhu, and L. Ma, "Class centroid alignment based domain adaptation for classification of remote sensing images," Pattern Recognition Letters, vol. 83, pp. 124-132, 2016. (SCI, T3) IF=3.255(Web of Science引用)
[13] C. Xing, L. Ma, and X. Yang, "Stacked denoise autoencoder based feature extraction and classification for hyperspectral images," Journal of Sensors, Article ID 3632943, 1:10, 2016. (Web of Science引用)
[14] L. Ma, M. M. Crawford, X. Yang, and Y. Guo, “Local manifold learning based graph construction for semisupervised hyperspectral image classification,” IEEE Transactions on Geoscience and Remote Sensing, vol. 53, no. 5, pp. 2832–2844, May 2015. (SCI, T2) IF=5.855(
[15] Li Ma, Melba. M. Crawford, and Jinwen Tian, “Local manifold learning-based k-nearest-neighbor for hyperspectral image classification”. IEEE Transactions on Geoscience and Remote Sensing, 2010, 48(11): 4099-4199. (SCI, T2) IF=5.855(
[16] Li Ma, Melba. M. Crawford, and Jinwen Tian, “Generalised supervised local tangent space alignment for hyperspectral image classification”. Electronics Letters, 2010, 46(7): 497-498. (SCI, T3) (引用
[17] L. Ma, Melba M. Crawford, and Jinwen Tian, “Anomaly detection for hyperspectral images based on robust locally linear embedding”. Journal of Infrared Millimeter and Terahertz Waves, 2010, 31(6): 753-762. (SCI, T4) (引用
[18] 邵远杰,吴国平,马丽*. 基于属类概率距离构图的半监督学习在高光谱遥感图像分类中的应用,测绘学报,2014, 43(11): 82-89.(
[19] 马丽*,鞠才,朱菲. 一种面向异常检测的高光谱图像降维算法,测绘科学,2015, 39(7).
[20] 王小攀,马丽*, 刘福江. 一种基于线性邻域传播的加权k近邻算法,计算机工程,2013, 39(7): 288-292.
[21] 马丽*,田金文. 基于局部能量最大可分的高光谱图像异常检测算法,遥感学报,2008,12(3): 420-427.
[22] 马丽*,常发亮,乔谊正,基于改进的均值漂移算法和粒子滤波算法的目标跟踪,模式识别与人工智能,2006,19,(6): 787-793. (EI)
期刊论文(合作作者):
[1] Jun Chen, Jiayi Ma, Changcai Yang, Li Ma, and Sheng Zheng. Non-rigid point set registration via coherent spatial mapping, Signal Processing, 2015,106: 62-72.(,T2)
[2] Jiayi Li, Hongyan Zhang, Liangpei Zhang, and Li Ma, “Hyperspectral anomaly detection by the use of background joint sparse representation,” IEEE Journal of Selected Topics in Applied Earth Observation and Remote Sensing, 2015,8(6): 2523-2533. (SCI,T2)
[3] Xiaoyong Bian, Xiaolong Zhang, Renfeng Liu, Li Ma, Xiaowei Fu. Adaptive classification of hyperspectral images using local consistency. Journal of Electronic Imaging, 2014, 23(6): 063014-1-17.)
[4] Faliang Chang, Li Ma, Yizheng Qiao, “Target tracking under occlusion by combining integral-intensity-matching with multi-block-voting,” Lecture Notes in Computer Science, ICIC 2005, 3644(1): 77-86. )
[5] 常发亮,马丽,乔谊正,复杂环境下基于自适应粒子滤波器的目标跟踪,电子学报,2006, 34(12):2150-2153. (EI)
[6] 常发亮,马丽,乔谊正,遮挡情况下基于特征相关匹配的目标跟踪算法,中国图象图形学报,2006,11(6):817-822.
[7] 常发亮,马丽,乔谊正,遮挡情况下的视觉目标跟踪方法研究,控制与决策,2006,21(5): 503-507. (EI)
[8] 常发亮,马丽,乔谊正,视频序列中面向人的多目标跟踪算法,控制与决策, 2007,22(4):418-422. (EI)
会议论文(第一作者、通讯作者):
[1] H. Wei, L. Ma, and X. Liu, “Multi-classifiers consistency based unsupervised manifold alignment for classification of remote sensing images,” IEEE International Geoscience and Remote Sensing Symposium, Waikoloa, HI, USA, September 26 - October 2, 2020, DOI: 10.1109/IGARSS39084. 2020.9323841. (研究生参加IGARSS会议)
[2] Z. Liu and L. Ma, “Class-wise adversarial transfer network for remote sensing scene classification,” IEEE International Geoscience and Remote Sensing Symposium, Waikoloa, HI, USA, September 26 - October 2, 2020, DOI: 10.1109/IGARSS39084.2020.9323406. (研究生参加IGARSS会议)
[3] D. Shen and L. Ma, “Cross-domain extreme learning machine for classification of hyperspectral images,” IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan, pp.3305-3308, 2019. (研究生参加IGARSS会议)
[4] Chuang Luo, Li Ma*, Neighbor Consistency Based Unsupervised Manifold Alignment for Classification of Remote Sensing Images, the 10th International Workshop on Pattern Recognition in Remote Sensing, 2018, Beijing, China. (研究生参加PRRS会议)
[5] Jiazhen Song, Li Ma*, Reconstruction based Transfer Network for Classifiction of Remote Senisng Image, the 10th International Workshop on Pattern Recognition in Remote Sensing, 2018, Beijing, China. (研究生参加PRRS会议)
[6] Andong Ma, Li Ma*. Multi-feature based Label Propagation for Semi-supervised Classification of Hyperspectral Data. IEEE Workshop on Hyperspectral Image and Signal Processing-Evolution in Remote Sensing, Swtzerland, Laussane, 2014. ((研究生参加Whispers会议)
[7] Xiaopan Wang, Li Ma*, Fujiang Liu. “Laplacian Support Vector Machine for Hyperspectral Image Classification by Using Manifold Learning Algorithms”. IEEE International Symposium on Geoscience and Remote Sensing, July, 1027, Australia,Melbourne, 2013.((研究生参加IGARSS会议)
[8] Li Ma*, Melba M. Crawford, and Jinwen Tian. “Anomaly detection for hyperspectral images using local tangent space alignment”. IEEE International Symposium on Geoscience and Remote Sensing, July, 824, Honolulu, Hawaii, USA, 2010. (