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学术论文

第一作者/通讯作者发表论文(SCI/EI):

2023年


1. W. Feng et al., "Hypothesis Margin-Based Ensemble Method for the Classification of Noisy Remote Sensing Data," in IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp. 1-21, 2023, Art no. 5408321, doi: 10.1109/TGRS.2023.3330568. (IF 8.2, 中科院1区)

2. Y. Cao, W. Feng#*, Y. Quan., "A Two-Step Ensemble-Based Genetic Algorithm for Land Cover Classification," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 16, pp. 409-418, 2023, doi: 10.1109/JSTARS.2022.3225665. (IF 5.5,中科院2区TOP,西电一类贡献度)

3. Yuan, X.; Liu, S.; Feng, W*.; Dauphin, G. Feature Importance Ranking of Random Forest-Based End-to-End Learning Algorithm. Remote Sens. 2023, 15, 5203. https://doi.org/10.3390/rs15215203 (IF 5.0, 中科院2区) 

4. Yuan, X.; Liang, Y.; Feng, W*.; Li, J.; Ren, H.; Han, S.; Liu, M. Classification of Coniferous and Broad-Leaf Forests in China Based on High-Resolution Imagery and Local Samples in Google Earth Engine. Remote Sens. 2023, 15, 5026. https://doi.org/10.3390/rs15205026 (IF 5.0, 中科院2区) 

5. Y. Song, W. Feng#* et al., "Ensemble Alignment Subspace Adaptation Method for Cross-Scene Classification," in IEEE Geoscience and Remote Sensing Letters, vol. 20, pp. 1-5, 2023, Art no. 5502705, doi: 10.1109/LGRS.2023.3256348. (IF 4.8, 中科院2区) 

6. L. Yang, W. Feng#* et al., "Radar-Infrared Sensor Fusion Based on Hierarchical Features Mining," in IEEE Signal Processing Letters, doi: 10.1109/LSP.2023.3341397. (IF 3.9, 中科院2区) 

7. W. Feng, Y. Long et al., "A review of addressing class noise problems of remote sensing classification," in Journal of Systems Engineering and Electronics, vol. 34, no. 1, pp. 36-46, February 2023, doi: 10.23919/JSEE.2023.000034. (SCI,国内期刊) 

8. 张亚丽,冯伟*,全英汇,邢孟道.基于多源遥感图像多级协同融合的舰船识别算法[J/OL].系统工程与电子技术:1-15[2023-02-13].

9. W. Feng, X. Gao et al., "Rotation XGBoost Based Method for Hyperspectral Image Classification with Limited Training Samples," 2023 IEEE International Conference on Image Processing (ICIP), Kuala Lumpur, Malaysia, 2023, pp. 900-904, doi: 10.1109/ICIP49359.2023.10223096.


                                                      2022年


1. S. Wang, W. Feng#*, Y. Quan, W. Bao, G. Dauphin, L. Gao, X. Zhong, and M. Xing, “Subfeature ensemble-based hyperspectral anomaly detection algorithm,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 15, pp. 5943–5952, 2022. IF 3.784,西电一类贡献度)

2. S. Wang, W. Feng#*, Y. Quan, Q. Li, G. Dauphin, W. Huang, J. Li, and M. Xing, “A heterogeneous double ensemble algorithm for soybean planting area extraction in google earth engine,” Computers and Electronics in Agriculture, vol. 197, p. 106955, 2022.IF 6.757中科院一区)

3. S. Dong, W. Feng#*, Y. Quan, G. Dauphin, L. Gao, M. Xing. "Deep Ensemble CNN Method Based on Sample Expansion for Hyperspectral Image Classification," in IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-15, 2022. (IF 8.125, 中科院一区).

4. Y. Cao, W. Feng#*, Y. Quan., "Forest Disaster Detection Method Based on Ensemble Spatial–Spectral Genetic Algorithm," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 15, pp. 7375-7390, 2022, doi: 10.1109/JSTARS.2022.3199539. IF 3.784,中科院三区)

5. X. Zou, W. Feng*, Y. Quan, Q. Li, G. Dauphin and M. Xing, "A Multi-Level Synergistic Image Decomposition Algorithm for Remote Sensing Image Fusion," IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium, Kuala Lumpur, Malaysia, 2022, pp. 3754-3757, doi: 10.1109/IGARSS46834.2022.9884942. (EI)

6. Song Y, Feng W*, Quan Y, et al. Remote Sensing Image Fusion Technology Based on DSP[C]//IGARSS 2022-2022 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2022: 3359-3362.(EI)
7. Zhang Y, Feng W*, Quan Y, et al. A Novel Spatial-Spectral Random Forest Algorithm for Pine WILT Monitoring[C]//IGARSS 2022-2022 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2022: 6045-6048.(EI)

8. 童莹萍, 冯伟*, 宋怡佳,全英汇, 黄文江, 高连如, 朱文涛, 邢孟道,面向不平衡高光谱遥感分类的SMOTE和旋转森林动态集成算法,遥感学报,2022.(EI)

9.钟娴, 冯伟*, 张亚丽,全英汇, 黄文江, 邢孟道,基于多样性特征协同技术的飓风前后森林破坏遥感监测,遥感学报,DOI:10.11834/jrs.20210230,2022. (EI)

10. 冯伟*, 龙以君, 王硕, 全英汇. 基于SMOTE和深度迁移卷积神经网络的多类不平衡遥感图像分类算法研究, 系统工程与电子技术, 2022.(EI)


2021年


  1. Q. Lv, Y. Quan*, W. Feng*, M. Sha, S. Dong and M. Xing, "Radar Deception Jamming Recognition based on Weighted Ensemble CNN with Transfer Learning," in IEEE Transactions on Geoscience and Remote Sensing, doi: 10.1109/TGRS.2021.3129645.
  2. Wei Feng, Yinghui Quan, Gabriel Dauphin, Qiang Li, Lianru Gao, Wenjiang Huang, Junshi Xia, Wentao Zhu, Mengdao Xing,Semi-supervised rotation forest based on ensemble margin theory for the classification of hyperspectral image with limited training data,Information Sciences,Volume 575,2021,Pages 611-638,ISSN 0020-0255,https://doi.org/10.1016/j.ins.2021.06.059.
  3. Q. Lv, W. Feng*, Y. Quan*, G. Dauphin, L. Gao and M. Xing, "Enhanced-Random-Feature-Subspace-Based Ensemble CNN for the Imbalanced Hyperspectral Image Classification," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 14, pp. 3988-3999, 2021, doi: 10.1109/JSTARS.2021.3069013.
  4. S. Dong, Y. Quan*, W. Feng*, G. Dauphin, L. Gao and M. Xing, "A Pixel Cluster CNN and Spectral-Spatial Fusion Algorithm for Hyperspectral Image Classification With Small-Size Training Samples," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 14, pp. 4101-4114, 2021, doi: 10.1109/JSTARS.2021.3068864.
  5. Quan Y, Tong Y, Feng W*, Dauphin G, Huang W, Zhu W, Xing M. Relative Total Variation Structure Analysis-Based Fusion Method for Hyperspectral and LiDAR Data Classification. Remote Sensing. 2021; 13(6):1143. https://doi.org/10.3390/rs13061143
  6. Yinghui Quan, Xian Zhong, Wei Feng *, Jonathan Cheung-Wai Chan , Qiang Li  and Mengdao Xing,“SMOTE-Based Weighted Deep Rotation Forest for the Imbalanced Hyperspectral Data Classification” ,Remote Sens. 2021, 13(3), 464; https://doi.org/10.3390/rs13030464
  7. Qinzhe Lv, Wei Feng*, Yinghui Quan, Qiang Li, Gabriel Dauphin, Lianru Gao, Guoping Zhao, Mengdao Xing, "Ensemble CNN with Enhanced Feature Subspaces for Imbalanced Hyperspectral Image Classification," 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 2021, pp. 3669-3672, doi: 10.1109/IGARSS47720.2021.9554677.
  8. S. Dong, Y. Quan, W. Feng*, Q. Li, G. Dauphin and M. Xing, "Ensemble CNN Based on Pixel-Pair and Random Feature Selection for Hyperspectral Image Classification with Small-Size Training Set," 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 2021, pp. 2353-2356, doi: 10.1109/IGARSS47720.2021.9555055.
  9. Y. Cao, W. Feng*, Y. Quan, A. Ren and M. Xing, "A Novel Forest Disater Monitoring Method Based on FCM and Neighborhood Factor Genetic Algorithm Using Multispectral Data," 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 2021, pp. 3761-3764, doi: 10.1109/IGARSS47720.2021.9553657.
  10. X. Zhong, Y. Quan, W. Feng*, Q. Li, G. Dauphin and M. Xing, "Imbalanced Multi-Class Classification of Hyperspectral Image Based on Smote and Deep Rotation Forest," 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 2021, pp. 2516-2519, doi: 10.1109/IGARSS47720.2021.9554960.
  11. Yingping Tong, Yinghui Quan,Wei Feng*,Gabriel Dauphin,Yong Wang, Puxia Wu, Mengdao Xing, "Multi-Scale Feature Extraction and Total Variation Based Fusion Method For HSI and Lidar Data Classification," 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 2021, pp. 5433-5436, doi: 10.1109/IGARSS47720.2021.9554337.

2020年


  1. Yinghui Quan, Xian Zhong, Wei Feng*, Gabriel Dauphin, Lianru Gao, Mengdao Xing, "A Novel Feature Extension Method for the Forest Disaster Monitoring Using Multispectral Data".Remote Sensing.2020,12, 2261.
  2. Yinghui Quan, Yingping Tong, Wei Feng*; Gabriel Dauphin, Wenjiang Huang, Mengdao Xing, "A Novel Image Fusion Method of Multi-Spectral and SAR Images for Land Cover Classification". Remote Sensing. 2020, 12(22), 3801; https://doi.org/10.3390/rs12223801.
  3. Wei Feng, Yinghui Quan, Gabriel Dauphin. Label Noise Cleaning with an Adaptive Ensemble Method Based on Noise Detection Metric. Sensors 2020, 20, 6718.
  4. Qiang Li, Wei Feng*, Yinghui Quan, "Trend and forecasting of the COVID-19 outbreak in China". J. Infect. 2020, 80, 469–496. 
  5. 王勇,靳伟昭,冯伟*,全英汇,基于改进R(2+1)D网络的暴力行为检测, 西安电子科技大学学报, 2020.
  6. W. Feng*, Y. Quan*, X. Zhong, G. Dauphin, M. Xing, W. Huang, "Two-step ensemble based class noise cleaning method for hyperspectral image classification," 2020 IEEE International Geoscience and Remote Sensing Symposium,Waikoloa, Hawaii, USA, 2020. (口头报告)
  7. W. Feng*, Y. Quan*, G. Dauphin, M. Xing,"Feature separation based rotation forest for hyperspectral image classification," 2020 IEEE International Geoscience and Remote Sensing Symposium,Waikoloa, Hawaii, USA, 2020.
  8. S. Dong, Y. Quan, W. Feng*, G. Dauphin, G. Zhao, Y. Wang, M. Xing, "Spectral-spatial feature extraction based CNN for hyperspectral image classification," 2020 IEEE International Geoscience and Remote Sensing Symposium,Waikoloa, Hawaii, USA, 2020.

2019年


  1. Wei Feng*, Gabriel Dauphin, Wenjiang Huang, Yinghui Quan, Wenzhi Liao,“New margin-based subsampling iterative technique in modified random forests for classification,” KnowledgeBased Systems, vol. 182, 2019.
  2. Wei Feng*, Gabriel Dauphin, Wenjiang Huang, Yinghui Quan, Wenxing Bao, Mingquan Wu, Qiang Li, “Dynamic synthetic minority over-sampling technique based rotation forest for the classification of imbalanced hyperspectral data,” IEEE Journal of Selected Topics in AppliedEarth Observations and Remote Sensing, vol. 12, no. 7, pp. 2159–2169, 2019.
  3. Wei Feng*, Wenjiang Huang and Wenxing Bao, "Imbalanced Hyperspectral Image Classification With an Adaptive Ensemble Method Based on SMOTE and Rotation Forest With Differentiated Sampling Rates,"  IEEE Geoscience and Remote Sensing Letters, vol. 16, no. 12, pp. 1879-1883, Dec. 2019.
  4. W. Feng*, W. Huang et al., "Ensemble margin based semi-supervised random forest for the classification of hyperspectral image with limited training data," 2019 IEEE International Geoscience and Remote Sensing Symposium,Yokohama, Japan, 2019, pp. 971-974. (口头报告)
  5. W. Feng*, S. Boukir and W. Huang, "Margin-Based Random Forest for Imbalanced Land Cover Classification," 2019 IEEE International Geoscience and Remote Sensing Symposium,Yokohama, Japan, 2019, pp. 3085-3088. 
  6. S. Boukir* and W. Feng*, "Identifying and Correcting Mislabeled Satellite Image Data by Iterative Ordering of Ensemble Margins," 2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan, 2019, pp. 3093-3096.

2012-2018年


  1. Wei Feng*; Wenjiang Huang; Jinchang Ren, "Class Imbalance Ensemble Learning Based on the Margin Theory". Appl. Sci. 2018, 8(5), 815;
  2. Wei Feng* and Wenxing Bao, "Weight-Based Rotation Forest for Hyperspectral Image Classification,"  IEEE Geoscience and Remote Sensing Letters, vol. 14, no. 11, pp. 2167-2171, Nov. 2017.
  3. W. Feng*, W. Huang, H. Ye and L. Zhao, "Synthetic Minority Oversampling Technique based Rotation Forest for the classification of unbalanced hyperspectral data," 2018 IEEE International Geoscience and Remote Sensing Symposium,Valencia, 2018, pp. 2651-2654. 
  4. W. Feng*, S. Boukir* and L. Guo,* "Identification and correction of mislabeled training data for land cover classification based on ensemble margin", 2015 IEEE International Geoscience and Remote Sensing Symposium, Milan, 2015, pp. 4991-4994. (口头报告)
  5. W. Feng* and S. Boukir*, "Class noise removal and correction for image classification using ensemble margin," 2015 IEEE International Conference on Image Processing (ICIP), Quebec City, QC, 2015, pp. 4698-4702. (口头报告,前10%最优论文)
  6. W. Bao*, W. Feng, R. Ma, "Remote sensing image classification based on support vector machine with the multi-scale segmentation" [C], Seventh International Conference on Graphic and Image Processing. International Society for Optics and Photonics, 2015. (实际完成人)
  7. W. Feng, W. Bao*. "A New Technology of Remote Sensing Image Fusion " [J]. Telkomnika, 2012, 10(3):551. (期刊文章)