Ruyi Feng received the B.S. degree in Geographic Information System from Hunan Normal University, Changsha, China, in 2011, and the M.S. degree in surveying and mapping engineering and the Ph. D. degree in photogrammetry and remote sensing from Wuhan University, Wuhan, China, in 2013 and 2016, respectively.

She has been with the School of Computer Science, China University of Geosciences (Wuhan) since 2016, and is currently an associate Professor. Her research interests include sparse representation, deep learning, hyperspectral image analysis, high-resolution remote sensing understanding, and intelligent interpretation of remote sensing imagery.

Over the past years, she has chaired and participated in more than 10 national projects dealing with remote sensing imagery, such as Major State Basic Research Development Program of China (973 Program), National High Technology Research and Development Program of China (863 Program),  National Natural Science Foundation of China (NSFC), Fundamental Research Funds for the Central University, China University of Geosciences (Wuhan), Open Reaserch of the Key Laboratory of Spectral Imaging Technology, Chinese Academy of Sciences.

Journal Publications:

      [1]      F. Li, R. Feng, W. Han, L. Wang*, “Ensemble model with cascade attention mechanism for high-resolution remote sensing image scene classification,” Optics Express, vol. 28, no. 12, pp. 22358-22387, 2020. (http://doi.org/10.1364/OE.395866) (SCI, IF=3.561)

[2]      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. (SCI, IF=5.910)

[3]      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)

[4]      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)

[5]      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)

[6]      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)

[7]      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)

[8]      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)

[9]      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)

[10]   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)

[11]   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)

[12]   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)

[13]   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)

[14]   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)

[15]   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)

[16]   R. Feng, Y. Zhong*, and L. Zhang, “Adaptive spatial regularization sparse unmixing strategy based on joint MAP for hyperspectral remote sensing imagery,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (JSTARS), vol. 9, no. 12, pp. 5791–5805, 2016. (SCI, IF=3.392)

[17]   Y. Zhong*, X. Wang, L. Zhao, R. Feng, L. Zhang and Y. Xu, “Blind spectral unmixing based on sparse component analysis for hyperspectral remote sensing imagery,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 119, pp. 49–63, 2016. (SCI, IF=6.942)

[18]   D. He, Y. Zhong*, R. Feng and L. Zhang, “Spatial-temporal subpixel mapping based on swarm intelligence theory”, Remote Sensing, vol. 8, no. 11, pp. 894, 2016. (SCI, IF=4.118)

[19]   R. Feng, Y. Zhong* and L. Zhang, “An improved non-local sparse unmixing algorithm for hyperspectral imagery,” IEEE Geoscience and Remote Sensing Letters (GRSL), vol. 12, no. 4, pp. 915-918, 2015. (SCI, IF=3.534)

[20]   R. Feng, Y. Zhong* and L. Zhang, “Adaptive non-local Euclidean medians sparse unmixing for hyperspectral imagery”, ISPRS Journal of Photogrammetry and Remote Sensing, vol. 97, pp. 9–24, 2014. (SCI, IF=6.942)

[21] Y. Zhong*, R. Feng and L. Zhang, “Non-local sparse unmixing for hyperspectral remote sensing imagery,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (JSTARS), vol. 7, no. 6, pp. 1889–1909, 2014. (SCI, IF=3.392)

Conference Papers

      [1]       H. Li, R. Feng, L. Wang, Y. Zhong, and L. Zhang, “Superpixel-based spatial constraints sparse unmixing for hyperspectral remote sensing imagery,” IGARSS 2020.

[2]       J. Bai, R. Feng, L. Wang, H. Li, F. Li, Y. Zhong, and L. Zhang, “Semi-supervised hyperspectral unmixing with very deep convolutional neural network,” IGARSS 2020.

[3]       W. Han, R. Feng, L. Wang, F. Li, and L. Wu, “A multi-stage network for improving the sample quality in Aerial image object detection,” IGARSS 2020.

[4]       J. Chen, R. Feng, L. Wang, W. Han, and J. Huang, “Multi-level strategy-based spatial information prediction for spatiotemporal remote sensing imagery fusion,” IGARSS 2020.

[5]       L. Cheng, L. Wang, and R. Feng, “Fractal characteristics and evolution of urban land-use: a case study in Shenzhen city,” IGARSS 2020.

[6]       Y. Wan, Y. Zhong, A. Ma, J. Wang, L. Zhang, and R. Feng, “RSSM-net: Remote sensing image scene classification based on multi-objective neural architecture search,” IGARSS 2020.

[7]       R. Feng, L. Wang and Y. Zhong, “Local block grouping with NAPCA spatial preprocessing for hyperspectral remote sensing imagery sparse unmixing,” in Proc. 2019 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), July 28-August 2, 2019, Yokohama, Japan.

[8]       Z. Liu, R. Feng, L. Wang, Y. Zhong and L. Cao, “D-RESUNET: ResUNet and dilated convolution for high resolution satellite imagery road extraction," in Proc. 2019 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), July 28-August 2, 2019, Yokohama, Japan.

[9]       W. Han, R. Feng, L. Wang and J. Chen, “Supervised generative adversarial network based sample generation for scene classification,” in Proc. 2019 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), July 28-August 2, 2019, Yokohama, Japan.

[10]   R. Fan, L. Wang, R. Feng and Y. Zhu, “Attention based residual network for high-Resolution remote sensing imagery scene classification,” in Proc. 2019 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), July 28-August 2, 2019, Yokohama, Japan.

[11]   Z. Chu, T. Tian, R. Feng and L. Wang, “Sea-land segmentation with RES-UNET and fully connected CRF,” in Proc. 2019 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), July 28-August 2, 2019, Yokohama, Japan.

[12]   R. Feng, T. Tian, X. Li and K. Sun, “Rolling guidance based scaled-aware spatial sparse unmixing for hyperspectral remote sensing imagery,” in Proc. 2018 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), July 22-27, 2018, Valencia, Spain.

[13]   W. Han, R. Feng, L. Wang and L. Gao, “Adaptive Spatial-Scale-Aware Deep Convolutional Neural Network for High-Resolution Remote Sensing Imagery Scene Classification,” in Proc. 2018 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), July 22-27, 2018, Valencia, Spain.

[14]   R. Feng, L. Wang, Y. Zhong and L. Zhang, “Differentiable sparse unmixing based on Bregman divergence for hyperspectral remote sensing imagery,” in Proc. 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), July23-28, 2017, Fort Worth, TX, USA.

[15]   X. Han, Y. Zhong, R. Feng and L. Zhang, “Robust geospatial object detection based on pre-trained faster R-CNN framework for high spatial resolution imagery,” in Proc. 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), July 23-28, 2017, Fort Worth, TX, USA.

[16]   R. Feng, D. He, Y. Zhong and L. Zhang, “Sparse representation based subpixel information extraction framework for hyperspectral remote sensing imagery,” in Proc. 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), July 10–15, 2016, Beijing, China.

[17]   R. Feng, Y. Zhong and L. Zhang, “Complete dictionary online learning for sparse unmixing,” in Proc. 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), July 10–15, 2016, Beijing, China.

[18]   Y. Zhong, Y. Wu, R. Feng, X. Xu and L. Zhang, “Non-local sub-pixel mapping for hyperspectral imagery,” in Proc. 2015 7th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), June 2-5, 2015, Tokyo, Japan.

[19]   R. Feng, Y. Zhong and L. Zhang, “Non-local Euclidean medians sparse unmixing for hyperspectral remote sensing imagery,” in Proc. 2014 IEEE International Geoscience and Remote Sensing Symposium and 35th Canadian Symposium on Remote Sensing (IGARSS), July 13–18, 2014, Quebec, Canada.

[20]   R. Feng, Y. Zhong and L. Zhang, “An improved weight-calculation non-local sparse unmixing for hyperspectral imagery,” in Proc. 2014 6th workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), June 24–27, 2014, Lausanne, Switzerland.

[21]   X. Xu, Y. Zhong, L. Zhang, H. Zhang and R. Feng, “A unified sub-pixel mapping model integrating spectral unmixing for hyperspectral imagery,” in Proc. 2013 5th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), June 26-28, 2013, Gainesville, FL, USA.

[22] R. Feng, Y. Zhong and L. Zhang, “Non-local sparse spectral unmixing for remote sensing imagery,” in Proc. 2012 4th workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), June 4–7, 2012, Shanghai, China.

Academic Activities 

Hornors, Awards & Certificates:

05/2020, the “Ten Outstanding Young Persons” Award, China University of Geosciences (Wuhan);

01/2019, the 10^th Young Teachers’ Teaching Competition, First prize;

09/2018, the “Surveying and Mapping Technology Progress Award”, First prize;

12/2017, the 4^th National College Mobile Internet Application Development and Innovation Competition, First prize(Top22/2760), Excellent Instructor;

07/2016, the “CUG Scholar Award”, China University of Geosciences (Wuhan);

05/2016: the “ Excellent Graduate Student Award”, Wuhan University;

10/2015: the “National scholarship”, Wuhan University;

10/2014: the “Guanghua Special Scholarship”, Wuhan University;

05/2013: the “Excellent Graduate Student Award”, Wuhan University;

05/2011: the “Excellent Graduate Student Award”, Wuhan University(5/100);

06/2010: the “National Motivational Scholarship”, Hunan Normal University(5/100);

06/2008: the “National Scholarship”, Hunan Normal University(2/300).

Professional Activities