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高光谱遥感图像智能处理
高分遥感图像智能处理与应用
城市遥感
稀疏表达理论
遥感图像深度学习理论研究
- 基于稀疏表达理论的高光谱遥感影像亚像元信息提取方法研究,2017-11-01,2018-12-31,智能地学信息处理湖北省重点实验室开放基金
- 基于深度增强学习的遥感大数据智能分析技术,2017-03-08,2018-03-31,中国科学院光谱成像技术重点实验室开放基金
- 多时相高光谱遥感影像稀疏亚像元信息提取方法研究,2017-08-15,2020-12-31,在研,国家自然科学基金青年基金
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- 期刊论文：
- [1] W. Han, L. Wang*, R. Feng*, L. Gao, X. Chen, Z. Deng, J. Chen, and P. Liu, “Sample generation based on a supervised Wasserstein generative adversarial network for high-resolution remote-sensing scene classification”, Information Sciences, vol. 539, pp. 177-194, 2020.
- [2] F. Li, R. Feng*, W. Han, L. Wang, “An augmentation attention mechanism for high-spatial-resolution remote sensing image scene classification”, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (JSTARS), doi: 10.1109/JSTARS.2020.3006241, 2020. (SCI, IF=3.392)
- [3] H. Li, R. Feng*, L. Wang, Y. Zhong, L. Zhang, “Superpixel-based reweighted low-rank and total variation sparse unmixing for hyperspectral remote sensing imagery”, IEEE Transactions on Geoscience and Remote Sensing, doi: 10.1109/TGRS.2020.2994260, 2020. (SCI, IF=5.630)
- [4] F. Li, R. Feng*, W. Han, and L. Wang, “High-resolution remote sensing image scene classification via key filter bank based on convolutional neural network”, IEEE Transactions on Geoscience and Remote Sensing, doi: 10.1109/TGRS.2020.2987060, 2020. (SCI, IF=5.630)