Han Gao | Radar Remote Sensing | Best Researcher Award

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Dr. Han Gao | Radar Remote Sensing | Best Researcher Award

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. πŸ“‘πŸ’»πŸ›°οΈ

Publication Profile

Orcid

Google Scholar

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. πŸ“‘πŸ’»πŸ›°οΈ

 

Research Focus πŸŒπŸ”¬

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. πŸŒΎπŸ“‘πŸŒ³

 

Publication Top Notes

  1. 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 πŸ“…πŸ“ˆ
  2. 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 πŸ“…πŸ“ˆ
  3. 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 πŸ“…πŸ“ˆ
  4. 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 πŸ“…πŸ“ˆ
  5. 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 πŸ“…πŸ“ˆ
  6. 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 πŸ“…πŸ“ˆ
  7. 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 πŸ“…πŸ“ˆ
  8. 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 πŸ“…πŸ“ˆ

Ali Mohammadzadeh | Remote Sensing Award | Best Researcher Award

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Prof. Ali Mohammadzadeh | Remote Sensing Award | Best Researcher Award

Prof. Ali Mohammadzadeh, K.N.Toosi University of Technology, Iran

Prof. Ali Mohammadzadeh is an accomplished educator and researcher specializing in LiDAR technology 🌐 and remote sensing applications in disaster management 🌍. With a Ph.D. in Civil-Surveying Engineering, he leads the LiDAR Laboratory at K.N. Toosi University of Technology. His expertise includes artificial intelligence πŸ€–, image processing, and calibration techniques. Serving as Faculty Vice-Dean and Associate Professor, he has extensive experience in academia, previously heading departments and contributing to various research projects. Prof. Mohammadzadeh’s work encompasses diverse fields such as bio-geomatics and evolutionary optimization, making significant contributions to both academia and industry.

 

Publication Profile

Orcid

Education πŸ“š

Prof. Ali Mohammadzadeh holds a Ph.D. in Civil-Surveying Engineering, earned in 2009 from K. N. Toosi University of Technology, Iran. Prior to this, he completed an M.Sc. (2003) and B.S. (2000) in Civil-Remote Sensing Engineering from the same institution.

Professional Experiences πŸ’Ό

Prof. Mohammadzadeh has held key roles at K.N.Toosi University of Technology, including Faculty Vice-Dean, Associate Professor, and Head of the LiDAR Laboratory and Photogrammetry & Remote Sensing Department.

Awards and Honors πŸ†

Ali Mohammadzadeh’s journey is marked by a trail of academic excellence and prestigious awards. From being recognized as an outstanding student at the National Intellect School to securing top ranks in Geomatics & Geodesy and Remote Sensing, his dedication has been unwavering. Throughout his career, he has been honored with multiple Remote Sensing Exemplary Researcher Awards, showcasing his exceptional contributions to the field. Notably, he received scholarships for PhD studies and international training, highlighting his commitment to continuous learning and global collaboration. His achievements culminated in being selected as one of the top researchers at K.N.Toosi University, a testament to his remarkable scholarly pursuits. πŸ…

Teaching ExperiencesΒ πŸŽ“

Prof. Ali Mohammadzadeh brings a wealth of teaching experience spanning over a decade, enriching the academic landscape with his expertise. His courses cover a broad spectrum, from foundational Principles of Remote Sensing to advanced topics like Optimization and Fuzzy Logic in Remote Sensing. With a focus on practical applications, he nurtures students in areas such as Airborne and Terrestrial LiDAR Technologies, equipping them with essential skills for real-world challenges. Prof. Mohammadzadeh’s commitment to mentorship shines through the graduation of numerous M.Sc. and Ph.D. students, supported by a diverse network of co-supervisors. His dedication extends to establishing cutting-edge laboratory facilities, fostering a collaborative environment for research and innovation. πŸŽ“

Research Focus

Prof. Ali Mohammadzadeh’s research focus spans a wide array of topics in remote sensing and geomatics, blending technological innovation with practical applications πŸ›°οΈ. His work encompasses post-disaster assessment using UAV data, relative radiometric normalization of remote sensing images, and forest change detection utilizing satellite imagery 🌳. He explores cutting-edge methods such as deep learning and machine learning algorithms to enhance data analysis and interpretation. Mohammadzadeh’s contributions extend to diverse domains including disaster management, environmental monitoring, and urban planning, reflecting his commitment to leveraging technology for societal benefit while advancing scientific knowledge 🌍.

Publication Top Notes

A Novel Weighted Ensemble Transferred U-Net Based Model (WETUM) for Postearthquake Building Damage Assessment From UAV Data: A Comparison of Deep Learning- and Machine Learning-Based Approaches

LIRRN: Location-Independent Relative Radiometric Normalization of Bitemporal Remote-Sensing Images

Synthetic Aperture Radar (SAR) for Ocean: A Review

BD-SKUNet: Selective-Kernel UNets for Building Damage Assessment in High-Resolution Satellite Images

Enhancing suspended sediment concentration retrieval by integrating thermal infrared and optical bands of Landsat-8 and machine learning algorithms

Distortion Robust Relative Radiometric Normalization of Multitemporal and Multisensor Remote Sensing Images Using Image Features

FCD-R2U-net: Forest change detection in bi-temporal satellite images using the recurrent residual-based U-net

Satellite-based monitoring of lake Urmia basin health with focus on its ecosystem regulating services by applying total ecosystem health (TEH) model

Individual tree detection from unmanned aerial vehicle (UAV) derived point cloud data in a mixed broadleaf forest using hierarchical graph approach

A Rapid Self-Supervised Deep-Learning-Based Method for Post-Earthquake Damage Detection Using UAV Data (Case Study: Sarpol-e Zahab, Iran)