Dr. Han Gao, China University of Petroleum (East China),China
Dr. Han Gao is an accomplished researcher at the College of Oceanography and Space Informatics, China University of Petroleum (East China). Specializing in radar remote sensing and microwave vision theory, his expertise extends to time series PolSAR data processing and remote sensing monitoring of flood disasters. Proficient in MATLAB, Python, and C++, he has developed innovative methods in crop classification and flood disaster monitoring, with significant applications in various Chinese provinces. Dr. Gao’s work has been published in top-tier journals like IEEE TGRS and RSE, earning substantial citations and recognition. 📡💻🛰️
Education
Dr. Han Gao pursued his academic journey at Central South University, where he obtained a Ph.D. in Photogrammetry and Remote Sensing from the College of Geosciences and Info-physics in June 2022. Prior to his doctorate, he completed a Master’s degree in Geomatics Engineering in June 2018, following his Bachelor’s degree in the same field in June 2015. His extensive education has laid a solid foundation for his research in remote sensing and geosciences. 📡💻🛰️
Research Focus 🌍🔬
Dr. Han Gao’s research primarily focuses on advanced remote sensing techniques, particularly in radar remote sensing and microwave vision theory. He has developed innovative methods for crop classification using time-series dual-polarization SAR datasets, integrating data from various sources like GF-3 PolSAR and Sentinel-2A. His work extends to flood disaster monitoring and the development of adaptive filters for PolSAR data. Dr. Gao’s research also includes forest height estimation and phase optimization for DS-InSAR. His significant contributions are published in high-impact journals, highlighting his expertise in agricultural and ecological remote sensing. 🌾📡🌳
Publication Top Notes
- A novel crop classification method based on ppfSVM classifier with time-series alignment kernel from dual-polarization SAR datasets – H Gao, C Wang, G Wang, H Fu, J Zhu – Remote Sensing of Environment 264, 112628 – 32 citations – 2021 📅📈
- A new crop classification method based on the time-varying feature curves of time series dual-polarization Sentinel-1 data sets – H Gao, C Wang, G Wang, Q Li, J Zhu – IEEE Geoscience and Remote Sensing Letters 17 (7), 1183-1187 – 30 citations – 2019 📅📈
- A crop classification method integrating GF-3 PolSAR and Sentinel-2A optical data in the Dongting Lake Basin – H Gao, C Wang, G Wang, J Zhu, Y Tang, P Shen, Z Zhu – Sensors 18 (9), 3139 – 28 citations – 2018 📅📈
- An adaptive nonlocal mean filter for PolSAR data with shape-adaptive patches matching – P Shen, C Wang, H Gao, J Zhu – Sensors 18 (7), 2215 – 21 citations – 2018 📅📈
- Forest height estimation using PolInSAR optimal normal matrix constraint and cross-iteration method – C Wu, C Wang, P Shen, J Zhu, H Fu, H Gao – IEEE Geoscience and Remote Sensing Letters 16 (8), 1245-1249 – 16 citations – 2019 📅📈
- TSPol-ASLIC: Adaptive superpixel generation with local iterative clustering for time-series quad-and dual-polarization SAR data – H Gao, C Wang, D Xiang, J Ye, G Wang – IEEE Transactions on Geoscience and Remote Sensing 60, 1-15 – 13 citations – 2021 📅📈
- A phase optimization method for DS-InSAR based on SKP decomposition from quad-polarized data – G Wang, B Xu, Z Li, H Fu, H Gao, J Wan, C Wang – IEEE Geoscience and Remote Sensing Letters 19, 1-5 – 13 citations – 2021 📅📈
- Fusion of spatially heterogeneous GNSS and InSAR deformation data using a multiresolution segmentation algorithm and its application in the inversion of slip distribution – H Yan, W Dai, H Liu, H Gao, WR Neely, W Xu – Remote Sensing 14 (14), 3293 – 5 citations – 2022 📅📈