高 峰

中国海洋大学计算机科学与技术学院,副教授
研究兴趣:遥感数据智能处理、多媒体计算
E-mail: gaofeng@ouc.edu.cn


代表性论文 [ Google Scholar 完整论文列表 ]

Journals:

2022

  1. Y. Meng, E. Rigall, X. Chen, F. Gao*, J. Dong* and S. Chen, "Physics-Guided Generative Adversarial Networks for Sea Subsurface Temperature Prediction," IEEE Transactions on Neural Networks and Learning Systems. (In Press and Accepted) (CCF B) [PDF] [Code@github]

  2. Y. Gan, F. Gao, J. Dong*, S. Chen, "Arbitrary-Scale Texture Generation from Coarse-Grained Control," IEEE Transactions on Image Processing, vol. 31, pp. 5841-5855, 2022. (CCF A) [PDF] [Code@github]

  3. D. Meng, F. Gao*, J. Dong, Q. Du and H. -C. Li, "Synthetic Aperture Radar Image Change Detection via Layer Attention-Based Noise-Tolerant Network," IEEE Geoscience and Remote Sensing Letters, vol. 19, 2022, Art no. 4026505. (CCF C) [PDF] [CODE]

  4. Y. Gan, X. Dong, H. Zhou, F. Gao and J. Dong*, "Learning the Precise Feature for Cluster Assignment," IEEE Transactions on Cybernetics, vol. 52, no. 8, pp. 8587-8600, Aug. 2022. (CCF B) [PDF]

  5. W. -S. Hu, H. -C. Li*, R. Wang, F. Gao, Q. Du and A. Plaza, "Pseudo Complex-Valued Deformable ConvLSTM Neural Network With Mutual Attention Learning for Hyperspectral Image Classification," IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-17, 2022, Art no. 5533017. (CCF B) [PDF]

  6. J. Wang, F. Gao*, J. Dong, Q. Du and H. Li, "Change Detection from Synthetic Aperture Radar Images via Dual Path Denoising Network," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 15, pp. 2667-2680, 2022. [PDF]

  7. L. Qi, F. Gao*, J. Dong*, X. Gao and Q. Du, "SSCU-Net: Spatial–Spectral Collaborative Unmixing Network for Hyperspectral Images," IEEE Transactions on Geoscience and Remote Sensing, vol. 60, 2022, Art no. 5407515. (CCF B) [PDF]

  8. J. Wang, F. Gao*, J. Dong, S. Zhang and Q. Du, "Change Detection From Synthetic Aperture Radar Images via Graph-Based Knowledge Supplement Network," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 15, pp. 1823-1836, 2022. [PDF] [Code@github]

  9. X. Qu, F. Gao*, J. Dong, Q. Du and H. -C. Li, "Change Detection in Synthetic Aperture Radar Images Using a Dual-Domain Network," IEEE Geoscience and Remote Sensing Letters, vol. 19, pp. 1-5, 2022, Art no. 4013405. (CCF C) [PDF] [Code@github]

  10. T. Zhang, F. Gao*, J. Dong and Q. Du, "Remote Sensing Image Translation via Style-Based Recalibration Module and Improved Style Discriminator," IEEE Geoscience and Remote Sensing Letters, vol. 19, pp. 1-5, 2022, Art no. 8009805. (CCF C) [PDF] [Code@github]

  11. Y. Ju, Y. Peng, M. Jian, F. Gao and J. Dong*, "Learning conditional photometric stereo with high-resolution features," Computational Visual Media, vol. 8, pp. 105-118, 2022. [PDF]

2021

  1. Y. Gao, F. Gao*, J. Dong, Q. Du and H. -C. Li, "Synthetic Aperture Radar Image Change Detection via Siamese Adaptive Fusion Network," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 14, pp. 10748-10760, 2021. [PDF] [Code@github]

  2. J. Wang, F. Gao*, J. Dong and Q. Du, "Adaptive DropBlock-Enhanced Generative Adversarial Networks for Hyperspectral Image Classification," IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 6, pp. 5040-5053, 2021. (CCF B) [PDF] [Code@github]

  3. Y. Gao, F. Gao*, J. Dong and H. -C. Li, "SAR Image Change Detection Based on Multiscale Capsule Network," IEEE Geoscience and Remote Sensing Letters, vol. 18, no. 3, pp. 484-488, March 2021. (CCF C) [PDF] [Code@github]

Former

  1. Y. Gao, F. Gao*, J. Dong and S. Wang, "Transferred Deep Learning for Sea Ice Change Detection From Synthetic Aperture Radar Images," IEEE Geoscience and Remote Sensing Letters, vol. 16, no. 10, pp. 1655-1659, Oct. 2019. (CCF C) [PDF] [Code@github]

  2. F. Gao, X. Wang, Y. Gao, J. Dong* and S. Wang, "Sea Ice Change Detection in SAR Images Based on Convolutional-Wavelet Neural Networks," IEEE Geoscience and Remote Sensing Letters, vol. 16, no. 8, pp. 1240-1244, Aug. 2019. (CCF C) [PDF] [Code@github]

  3. Y. Gao, F. Gao*, J. Dong and S. Wang, "Change Detection From Synthetic Aperture Radar Images Based on Channel Weighting-Based Deep Cascade Network," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 12, no. 11, pp. 4517-4529, Nov. 2019. [PDF] [Code@github]

  4. F. Gao, Q. Wang, J. Dong*, S. Wang, "Spectral and spatial classification of hyperspectral images based on random multi-graphs," Remote Sensing, 2018. [PDF] [Code@github]

  5. F. Gao, X. Wang, J. Dong*, S. Wang, "SAR image change detection based on frequency domain analysis and random multi-graphs," Journal of Applied Remote Sensing, 2018. [PDF] [Code@github]

  6. F. Gao, J. Dong*, B. Li, Q. Xu, C. Xie, "Change detection from synthetic aperture radar images based on neighborhood-based ratio and extreme learning machine," Journal of Applied Remote Sensing, 2016. [PDF] [Code@github]

  7. F. Gao, J. Dong*, B. Li and Q. Xu, "Automatic Change Detection in Synthetic Aperture Radar Images Based on PCANet," IEEE Geoscience and Remote Sensing Letters, vol. 13, no. 12, pp. 1792-1796, Dec. 2016. (CCF C) [PDF] [Code@github]

Conferences:

  1. S. Xiu, F. Gao*, Y. Chen, "Residual Multi-resolution Network for Hyperspectral Image Denoising," Image and Graphics Technologies and Applications (IGTA), 2021.
  2. M. Feng, F. Gao*, J. Fang, J. Dong "Hyperspectral and LiDAR data classification based on linear self-attention," IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2021, pp. 2401-2404.
  3. Y. Gao, F. Gao*, J. Dong, "Hyperspectral Image Denoising Based On Multi-Stream Denoising Network," IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2021, pp. 2158-2161.
  4. W. Liu, F. Gao*, J. Dong, "Disentangled Non-Local Network for Hyperspectral and LiDAR Data Classification," IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2021, pp. 2397-2400.
  5. T. Zhang, Y. Gao, F. Gao, L. Qi, J. Dong*, "Arbitrary Style Transfer with Parallel Self-Attention," International Conference on Pattern Recognition (ICPR), 2020, pp. 1406-1413.
  6. J. Wang, F. Gao*, J. Dong, "Change detection from SAR images based on deformable residual convolutional neural networks," Multimedia Asia, 2020.

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