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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, 国家自然科学基金项目, 2017/08/15-2020/12/31, 在研, 国家自然科学基金青年基金
- 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)
- [5] R. Feng, L. Wang*, Y. Zhong, “Joint local block grouping with noise-adjusted principal component analysis for hyperspectral remote sensing imagery sparse unmixing”, Remote Sensing, vol. 11, no. 10, pp. 1223, 2019. (SCI, IF=4.118)
- [6] M. Song, Y. Zhong*, A. Ma, R. Feng, “Multiobjective sparse subpixel mapping for remote sensing imagery,” IEEE Transactions on Geoscience and Remote Sensing, vol. 57, no. 7, pp. 4490-4508, 2019. (SCI, IF=5.630)
- [7] K. Xu, X. Wang, C. Kong*, R. Feng, G. Liu, C. Wu, “Identification of Hydrothermal Alteration Minerals for Exploring Gold Deposits Based on SVM and PCA Using ASTER Data: A Case Study of Gulong,” Remote Sensing, vol. 11, no. 24, pp. 3003, 2019. (SCI, IF=4.118)
- [8] D. AL-Alimi, Y. Shao, R. Feng, M. A. Al-qaness, M. A. Elaziz, S. Kim*, “Multi-scale geospatial object detection based on shallow-deep feature extraction”, Remote Sensing, vol. 11, no. 21, pp. 2525, 2019. (SCI, IF=4.118)
- [9] Z. Chen, Y. Wang, W. Han*, R. Feng*, J. Chen, “An Improved pretraining strategy-based scene classification with deep learning,” IEEE Geoscience and Remote Sensing Letters (GRSL), DOI: 10.1109 / LGRS.2019.2934341,2019. (SCI, IF=3.534)
- [10] R. Feng, L. Wang*, Y. Zhong, “Least angle regression-based constrained sparse unmixing of hyperspectral remote sensing imagery”, Remote Sensing, vol. 10, no. 10, pp. 1546, 2018. (SCI, IF=4.118)
- [11] R. Feng, Y. Zhong*, L. Wang*, and W. Lin*, “Rolling guidance based scale-aware spatial sparse unmixing for hyperspectral remote sensing imagery,” Remote Sensing, vol. 9, no. 12, pp. 1218, 2017. (SCI, IF=4.118)
- [12] W. Han, R. Feng, L. Wang*, Y. Cheng, “A semi-supervised generative framework with deep learning features for high-resolution remote sensing image scene classification,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 145, pp. 23–43, 2018. (SCI, IF=6.942)
- [13] R. Feng, Y. Zhong*, X. Xu and L. Zhang, “Adaptive sparse subpixel mapping with a total variation model for remote sensing imagery,” IEEE Transactions on Geoscience and Remote Sensing, vol. 54, no. 5, pp. 2855–2872, 2016. (SCI, IF=5.630)
- [14] R. Feng, Y. Zhong*, Y. Wu, D. He, X. Xu and L. Zhang, “Nonlocal total variation subpixel mapping for hyperspectral remote sensing imagery”, Remote Sensing, vol. 8, no. 3, pp. 250, 2016. (SCI, IF=4.118)