Lecturer at Nanjing University of Information Science and Technology | China
Mr. Jie Han is an academic researcher specializing in mathematical modeling, surveying engineering, and remote sensing image processing. His expertise lies in developing advanced algorithms for errors-in-variables models, optical image restoration, and ill-posed inversion problems. He has consistently contributed to high-impact publications in leading journals such as IEEE Transactions on Geoscience and Remote Sensing, Remote Sensing, and Information Sciences, which reflect his commitment to advancing knowledge and practice in his field. Dr. Han has participated in prominent international conferences, sharing his research and collaborating with peers from diverse backgrounds. As a lecturer at Nanjing University of Information Science and Technology, he combines teaching with active research, mentoring young scholars, and fostering innovation. His scholarly contributions have not only enhanced theoretical approaches but also supported practical applications in geoinformatics and environmental monitoring. With an expanding academic portfolio, Dr. Han continues to establish himself as a promising researcher with international impact.
Professional Profile
ORCID Profile
Education
Mr. Jie Han has pursued an extensive academic journey in the fields of surveying engineering, geoinformatics, and remote sensing. He began his undergraduate studies in Surveying and Mapping Engineering at Hefei University of Technology, where he gained a solid foundation in geospatial sciences, cartography, and mathematical modeling. He later continued his academic career by pursuing doctoral studies at Tongji University in Shanghai, one of China’s leading institutions in engineering and geoscience research. There, he specialized in surveying and geoinformatics, focusing on errors-in-variables models and advanced statistical approaches for solving ill-posed problems. His doctoral research contributed significantly to advancements in precision estimation, image restoration, and hyperspectral data analysis. Following his doctoral degree, he transitioned into academia as a lecturer at the School of Mathematics and Statistics at Nanjing University of Information Science and Technology. His educational background combines rigorous training in engineering and mathematics, which serves as the basis for his diverse research contributions.
Professional Experience
Mr. Jie Han has developed his professional career through a balanced combination of teaching, research, and scholarly collaboration. He currently serves as a lecturer at the School of Mathematics and Statistics at Nanjing University of Information Science and Technology, where he contributes to both undergraduate and graduate-level education while supervising research activities. In his role, he integrates theoretical knowledge with practical research applications, fostering academic growth among students and peers. Prior to this appointment, his professional experience was shaped by his doctoral training at Tongji University, where he engaged in several collaborative projects in surveying, image processing, and statistical modeling. He has also actively participated in international conferences such as IEEE IGARSS and ISPRS, presenting his research and establishing academic connections worldwide. His professional journey reflects a consistent dedication to advancing geoinformatics and remote sensing applications while contributing to the global academic community through research dissemination and academic service.
Research Interest
Mr. Jie Han’s research interests lie at the intersection of mathematical modeling, geoinformatics, and remote sensing image analysis. A central area of his work focuses on errors-in-variables models, where he explores improved estimation methods for solving statistical and mathematical challenges in measurement and modeling. He is also deeply engaged in the development of algorithms for optical image restoration, including image denoising, deblurring, and dehazing, which have direct applications in enhancing the quality and usability of remote sensing data. His research extends to ill-posed inversion problems, where he investigates solutions for complex data reconstruction in radar and hyperspectral imaging. Beyond theory, Dr. Han is interested in practical applications such as environmental monitoring, image-based mapping, and atmospheric studies. By combining statistical analysis with computational techniques, his research bridges theoretical foundations with real-world challenges, contributing to advancements in geospatial sciences, remote sensing technologies, and interdisciplinary applications across scientific and engineering domains.
Award and Honor
Mr. Jie Han has been recognized for his academic achievements through his publications in highly reputed journals and his participation in international conferences that highlight the significance of his contributions. The acceptance of his work in leading journals such as IEEE Transactions on Geoscience and Remote Sensing, Remote Sensing, and Information Sciences serves as a mark of distinction, as these platforms are highly competitive and widely respected in the scientific world. Additionally, his role as first and corresponding author in multiple publications showcases his leadership and initiative in research. His invited participation in academic gatherings such as IEEE IGARSS and ISPRS further highlights his growing visibility in the global scientific arena. With continued engagement and expansion of his research influence, future recognitions and awards are strongly anticipated.
Research Skill
Mr. Jie Han possesses a wide range of research skills that combine mathematical expertise with computational and applied techniques. He is proficient in designing and applying errors-in-variables models for statistical estimation problems, offering innovative methods for handling uncertainty in measurements. His skills in optical image processing include advanced methods for denoising, deblurring, and dehazing, which significantly improve the quality of remote sensing data. He is also experienced in addressing ill-posed inversion problems using sparse regularization and tensor-based techniques, which are essential for solving complex imaging challenges. His technical competence extends to hyperspectral image analysis, radar imaging, and environmental data modeling. In addition, he has strong analytical and programming skills, enabling the development of algorithms and models that bridge theory with real-world applications. His experience in writing and publishing high-quality research papers also demonstrates his ability to communicate complex concepts effectively, making his skills both versatile and impactful in the scientific community.
Publications Top Notes
Title: Novel Regularization Method of Adaptively Balanced L1 and L2 Norms with Bias Removal
Authors: Jie Han; Zhichao Zhang; Shouzhu Zheng; Qing Fu; Wenping Song; Haiyong Ding; Minghua Wang; Weiguo Huang
Year: 2025
Title: Comparison of Posterior Precision Estimation Methods of Weighted Total Least-Squares Solution for Errors-in-Variables Model
Authors: Jie Han; Songlin Zhang; Shimeng Dong; Qingyun Yan
Year: 2024
Title: A Nonblind Deconvolution Method by Bias Correction for Inaccurate Blur Kernel Estimation in Image Deblurring
Authors: Jie Han; Songlin Zhang; Zhen Ye
Year: 2023
Title: Double-Factor Tensor Cascaded-Rank Decomposition for Hyperspectral Image Denoising
Authors: Jie Han; Chuang Pan; Haiyong Ding; Zhichao Zhang
Year: 2023
Title: Automatic Registration of Very Low Overlapping Array InSAR Point Clouds in Urban Scenes
Authors: Xiaohua Tong; Xin Zhang; Shijie Liu; Zhen Ye; Yongjiu Feng; Huan Xie; Longyong Chen; Fubo Zhang; Jie Han; Yanmin Jin et al.
Year: 2022
Title: Bias Analysis and Correction for Ill-Posed Inversion Problem with Sparsity Regularization Based on L1 Norm for Azimuth Super-Resolution of Radar Forward-Looking Imaging
Authors: Jie Han; Songlin Zhang; Shouzhu Zheng; Minghua Wang; Haiyong Ding; Qingyun Yan
Year: 2022
Title: Local Patchwise Minimal and Maximal Values Prior for Single Optical Remote Sensing Image Dehazing
Authors: Jie Han
Year: 2022
Title: Spatiotemporal PM2.5 Estimations in China from 2015 to 2020 Using an Improved Gradient Boosting Decision Tree
Authors: Weihuan He; Huan Meng; Jie Han; Gaohui Zhou; Hui Zheng; Songlin Zhang
Year: 2022
Title: Closure to “New First-Order Approximate Precision Estimation Method for Parameters in an Errors-in-Variables Model”
Authors: Jie Han; Songlin Zhang; Jingchang Li
Year: 2022
Title: Indoor Map Boundary Correction Based on Normalized Total Least Squares of Condition Equation
Authors: Jie Han
Year: 2021
Title: Quality Evaluation of Linear Inequality Constrained Estimation by Monte Carlo Sampling in Parameter Space
Authors: Songlin Zhang; Jingchang Li; Kun Zhang; Jie Han
Year: 2021
Title: New First-Order Approximate Precision Estimation Method for Parameters in an Errors-in-Variables Model
Authors: Jie Han; Songlin Zhang; Jingchang Li
Year: 2021
Title: A General Partial Errors-in-Variables Model and a Corresponding Weighted Total Least-Squares Algorithm
Authors: Jie Han; Songlin Zhang; Yali Li; Xin Zhang
Year: 2020
Conclusion