Welcome! I am now a Ph.D student in Geosensing Systems Engineering and Sciences at the University of Houston. My current research focuses on remote sensing of hydrology, with a specific emphasis on flood inundation forecasting. For more information on my research interests and experience, check out my research page!

📖 Educations

• Ph.D. in Geosensing Systems Engineering and Sciences, University of Houston, U.S., Present.

  • Advisor: Dr. Hyongki Lee

• M.Sc. in Communication Engineering, University of Electronic Science and Technology of China, China, 2024.

  • Advisor: Dr. Haohao Ren

• B.Sc. in Communication Engineering, Hefei University, China, 2021.

📝 Full Publications

Journal Papers:

• IEEE Sensors Journal Threshold-free Open-set Learning Network for SAR Automatic Target Recognition,Yue Li, Haohao Ren, Xuelian Yu, Chengfa Zhang, Lin Zou, Yun Zhou

  IEEE Sensors Journal, 2024. Paper

Conference Papers:

• IGARSS 2024 Pseudo-unknown Class Guided-based Open-set Learning Network for SAR Automatic Target Recognition, Yue Li, Haohao Ren, Xuelian Yu, Chengfa Zhang, Lin Zou, Yun Zhou

  2024 IEEE International Geoscience and Remote Sensing Symposium. Paper

• IGARSS 2024 Layer-wise Representative Exemplar Selection-based Incremental Learning fo SAR Target Recognition, Rongsheng Zhou, Haohao Ren, Yue Li, Fulu Dong, Yun Zhou, Lin Zou

  2024 IEEE International Geoscience and Remote Sensing Symposium. Paper

• IGARSS 2024 Global-local Information Interactive Learning Network for SAR Target Recognition With Limited Sample, Lei Miao, Haohao Ren, Yue Li, Lin Zou, Xuegang Wang

  2024 IEEE International Geoscience and Remote Sensing Symposium. Paper

📝 Research Experience

Open set recognition based on a hyperspherical embedding network

• Model extracted features onto a unit hypersphere with von Mises-Fisher (vMF) distribution to improve the inter-class separability of feature representations.

• Measure the similarity of samples to each class center and design a discrimination criterion to detect unknown targets based on this metric.

• Calibrate the predicted probability based on uncertainty estimation.

• Implement the algorithm and evaluate the performance on two public satellite imagery datasets (MSTAR).

A threshold-free open-set recognition network for satellite imagery

• Propose a threshold-free open-set learning network for satellite target recognition.

• Improve the network structure of GAN to formulate the open-set recognition problem as a K+1 classification task.

• Implement the algorithm, evaluate the performance, and compare it with four SOTA models on two public satellite datasets (MSTAR and SAMPLE).

🎖 Honors and Awards

• 2022 Outstanding Graduate Student
• 2021 Outstanding Graduate of Anhui Province
• 2019 University Special Prize Scholarship
• 2018 University Special Prize Scholarship