中国地质大学(武汉)导师教师师资介绍简介-冯如意

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副教授硕士生导师
主要任职：计算机学院人工智能系党支部组织委员
曾获荣誉：2020年，第十四届“中国地质大学十大****”；
2019年，中国地质大学（武汉）第十届青年教师教学竞赛一等奖；
2018年，高光谱遥感数据分类关键技术及其典型应用，中国测绘学会测绘科技进步奖，一等奖；
2017年，第四届全国高校移动互联网应用开发创新大赛，优秀指导老师，一等奖；
2016年，SAIL青少年科技创新大赛最佳导师推荐奖。
性别：女
毕业院校：武汉大学
学历：博士研究生
学位：博士学位
在职信息：在职
所在单位：计算机学院
入职时间：2016-07-08
职务：副教授
学科：空间信息与数字技术遥感科学与技术
办公地点：未来城校区科一楼432
联系方式：fengryATcugDOTeduDOTcn
Email：

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个人简历冯如意，中国地质大学（武汉）计算机学院副教授，硕士生导师。2016年毕业于武汉大学测绘遥感信息工程国家重点实验室，获摄影测量与遥感专业工学博士学位。

主要研究方向包括：高光谱遥感图像智能处理、高分遥感城市智能分析与应用等，研究兴趣包括高光谱遥感图像亚像元信息提取、高分遥感图像深度学习理论研究等。IEEE Member, CCF会员，VALSE第三届VOOC委员。
近年来主持国家自然科学基金青年基金、国防军科委项目、中国科学院光谱成像技术重点实验室开放基金、湖北省智能地学信息处理重点实验室开放基金等，在ISPRS、TGRS、JSTARS等遥感领域国际权威期刊发表论文20余篇（其中，一区4篇，二区14篇，ESI论文1篇），EI检索国际遥感顶级会议论文20余篇；担任ISPRS、TGRS、JSTARS、IGRSL等期刊审稿员，作为主要发明人申报发明专利4项，其中国防专利2项，获测绘科技进步奖一等奖等。

期刊论文： [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. (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.**, 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.**, 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.**, 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.**,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)

会议论文：[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.

教育经历 [1] 2013.9-2016.6
武汉大学 | 遥感科学与技术 | 工学博士学位 | 博士研究生
[2] 2011.9-2013.6
武汉大学 | 测绘工程 | 硕士学位 | 硕士研究生
[3] 2007.9-2011.6
湖南师范大学 | 地理信息科学 | 学士学位 | 本科(学士)

工作经历 [1] 2016.7-至今
中国地质大学（武汉） |计算机学院

社会兼职 [1]VALSE VOOC Member
[2]CCF Member
[3]IEEE Member

研究方向 [1] 高光谱遥感图像处理

[2] 高分遥感图像智能分析与应用

[3] 遥感图像深度学习理论研究

[4] 稀疏表达理论

[5] 城市遥感

联系方式 [1]邮编：

[2]传真：

[3]通讯/办公地址：

[4]办公室电话：

[5]移动电话：

[6]邮箱：

团队成员 智能遥感解译与应用
主要由王力哲教授及冯如意副教授负责指导，由学院优秀的博士研究生、硕士研究生及高年级本科生组成的青年科研突击团队。
主要研究方向包括：高光谱遥感影像分类、分解、降维、目标探测、异常探测；高分辨遥感影像场景理解、分类、目标检测；多源多时相遥感影像多特征融合。
目前，团队优秀的成果已投稿或发表在国际计算机领域顶级会议、期刊以及国际遥感领域顶级会议及期刊；申请并完成了多项遥感智能解译项目。

多源遥感大数据智能处理
团队由王力哲教授作为学术总指导，由计算机学院的青年副教授、副研究员、讲师、博士后组成的具有一定梯度的青年科研团队。
主要研究方向包括：高光谱遥感数据分析与解译、高分辨率遥感图像的多特征表达与融合、高分辨遥感数据智能处理与分析、多角度影像三维匹配与重建、时空大数据智能处理与分析以及无人机遥感智能解译等方向。
目前，研究成果数十项科研成果投稿或发表在国际遥感领域或多媒体应用等顶级期刊；团队合作承担或完成多项国家级及省部级遥感相关项目。

中国地质大学(武汉)导师教师师资介绍简介-冯如意

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