Yifei Yin | Speckle noise suppression in SAR images | Research Excellence Award

Dr. Yifei Yin | Speckle noise suppression in SAR images | Research Excellence Award

Beijing Institute of Technology | China 

The research work focuses on the intelligent interpretation of synthetic aperture radar imagery, with particular emphasis on end-to-end understanding of satellite-based SAR data. Core research activities include SAR image pre-processing, Speckle noise suppression in SAR images speckle noise suppression, and robust target detection and recognition under complex imaging conditions. A key scientific contribution lies in addressing the limitations of conventional supervised learning approaches, which typically rely on clean reference images that are rarely available in real-world SAR scenarios. To overcome this challenge, a self-supervised despeckling framework was proposed, enabling effective network training using only intensity SAR images without the need for external ground-truth data. This strategy significantly enhances the practicality and scalability of deep learning methods for operational SAR systems. The research further contributes to improving feature preservation and structural consistency in despeckled images, which directly benefits downstream tasks such as object recognition and scene understanding. In addition, the work actively supports national-level research and development initiatives, fostering collaboration across multidisciplinary teams in remote sensing, signal processing, and artificial intelligence. Overall, these contributions advance the reliability, adaptability, and real-world applicability of intelligent SAR image interpretation, strengthening its role in satellite observation, surveillance, and Earth monitoring applications.

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Self-supervised despeckling based solely on SAR intensity images: A general strategy


– ISPRS Journal of Photogrammetry and Remote Sensing, 2026

Jinfa Zhang | Statistical Applications in Engineering | Research Excellence Award

Dr. Jinfa Zhang | Statistical Applications in Engineering | Research Excellence Award

China University of Petroleum (Beijing) | China 

The research profile reflects a strong and continuous focus on petroleum engineering, Statistical Applications in Engineering with specialized expertise in rock mechanics, geomechanics, lost circulation control, reservoir stimulation, and enhanced oil and gas recovery. Advanced doctoral research concentrates on the mechanical behavior of reservoir rocks, wellbore stability, and lost circulation mechanisms, integrating theoretical modeling with practical engineering applications. Master’s-level research emphasized oil and gas reservoir stimulation technologies, enhanced recovery methods, numerical reservoir simulation, and optimization techniques, supported by a strong academic performance and rigorous coursework in advanced reservoir engineering, fluid phase equilibria, and simulation software applications. Undergraduate training provided a solid foundation in drilling engineering, completion engineering, rock mechanics, porous media flow, oilfield chemistry, and production engineering. The research experience is complemented by extensive proficiency in industry-standard professional software for fracturing design, reservoir simulation, curve fitting, programming, and geospatial analysis, enabling comprehensive data-driven studies. Practical exposure through geological fieldwork and petroleum production training strengthened the ability to connect theoretical research with field-scale operations. Academic excellence is demonstrated through competitive scholarships, innovation and design competitions, and national-level recognitions, highlighting strong research capability, interdisciplinary technical skills, and potential for impactful contributions to petroleum engineering research and technology development.

Citation Metrics (Scopus)

40
30
20
10
0

Citations
23

Documents
9

h-index
2

Citations

Documents

h-index


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