Lianhui Liang | Deep learning or Image processing | Excellence in Research Award

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Dr. Lianhui Liang | Deep learning or Image processing | Excellence in Research Award

Guangxi University | China 

Lianhui Liang is an academic researcher in electrical and information engineering with a strong focus on intelligent remote sensing analysis and advanced signal interpretation. His work centers on hyperspectral, multispectral, and LiDAR data understanding, Deep learning or Image processing  with applications spanning environmental monitoring, land surface analysis, and complex scene interpretation. His research integrates signal processing, pattern recognition, and deep learning techniques to enhance feature extraction, classification accuracy, and information fusion in high-dimensional remote sensing imagery. He has contributed significantly to spectral intelligence, including algorithm development for image inversion, target detection, and data fusion across heterogeneous sensors. His interdisciplinary approach bridges theoretical modeling with practical engineering applications, particularly in optical and microwave remote sensing. Through sustained collaboration with international research groups, his work reflects a global perspective on emerging challenges in remote sensing and artificial intelligence. He has actively engaged in advanced training programs related to remote sensing data processing, deep learning frameworks, and intelligent interpretation systems, strengthening the transfer of cutting-edge methods into applied research. His scholarly contributions include peer-reviewed publications, intellectual property development, and participation in research and development projects supported by public and industrial partners. Overall, his research advances intelligent remote sensing systems and contributes to the broader fields of geospatial analytics and artificial intelligence-driven Earth observation.

Citation Metrics (Google Scholar)

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Citations
271

Documents
7

h-index
7

Citations

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View Google Scholar Profile

Featured Publications


Prototype Similarity-Constraint Enhancement Network: A Few-Shot Class-Incremental Learning for Hyperspectral Image Classification

– Expert Systems with Applications, 2025
L. Yang, Y. Tan, L. Liang, H. Xu, T. Wu, Z. Huang, X. Li, Y. Tang

Cross-Stage Attention Edge Enhancement and Fourier-Wavelet Transformer Integrated Network for Hyperspectral Image Classification

– IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2025
L. Liang, S. Yuan, Y. Zeng, Y. Lin, Y. Zhang, P. Xie, T. X. Wu

LHCF: Liquid Neural Network with Hierarchical Collaborative Fusion for Hyperspectral Image Classification

– Authorea Preprints, 2025
L. Liang, J. Zhang, S. Zhang, B. Tu, L. Yang, J. Li, A. Plaza

LKMA: Learnable Kernel and Mamba with Spatial-Spectral Attention Fusion for Hyperspectral Image Classification

– IEEE Transactions on Geoscience and Remote Sensing, 2025
L. Liang, J. Zhang, P. Duan, X. Kang, T. X. Wu, J. Li, A. Plaza

Cross-Domain Few-Shot Hyperspectral Image Classification with Local Entropy Adaptation Metric

– Authorea Preprints, 2025
Y. Zhang, Z. Liu, P. Duan, L. Lian

Xiaoqing Wan | Pattern Recognition | Research Excellence Award

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Dr. Xiaoqing Wan | Pattern Recognition | Research Excellence Award

Hengyang Normal University | China

Xiaoqing Wan is a lecturer in computer science and an active member of the global research community in Pattern Recognition artificial intelligence and intelligent information processing. His academic work focuses on pattern recognition and image processing, with particular emphasis on the development of advanced algorithms for remote sensing image analysis. His research integrates deep learning and machine learning techniques to improve classification accuracy, feature extraction, and robustness in complex and large-scale image datasets. In addition, he is deeply involved in the design of computer-aided diagnosis systems, where artificial intelligence is applied to support medical image interpretation and decision-making, aiming to enhance efficiency and reliability in clinical analysis. His scholarly background in signal and information processing and communication systems provides a strong theoretical foundation for interdisciplinary research that bridges engineering, data science, and applied intelligence. As an educator, he contributes to the training of future engineers and researchers through teaching core subjects in artificial intelligence, programming, and software engineering, with a strong focus on practical problem-solving and algorithmic thinking. His ongoing research continues to explore innovative methodologies that combine intelligent computation with real-world applications, contributing to the advancement of intelligent systems in remote sensing, healthcare, and computer vision.

Citation Metrics (Scopus)

400
300
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Citations
173

Documents
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h-index
6

Citations

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View Scopus Profile

Featured Publications

Kostiantyn Kotenko | Operations Research and Statistical Optimization | Best Researcher Award

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Prof. Kostiantyn Kotenko | Operations Research and Statistical Optimization | Best Researcher Award

S.P.Timoshenko Institute of Mechanics | Ukraine

Dr. Kostyantin Kotenko is an Professor in the Department of Theoretical Mechanics at the Kyiv National University of Construction and Architecture (KNUCA), Ukraine, specialising in building structures and civil engineering systems. He holds the degree of Candidate of Technical Sciences (equivalent to PhD) and has developed extensive expertise in the dynamics of layered, or sandwich, shell structures with inhomogeneous fillers. A graduate of KNUCA, Dr. Kotenko’s academic background is rooted in the theory and design of complex structural systems, and his research focuses on the dynamic response and stability of multi-layered shells subjected to transient, impact, and nonstationary loads. Over his career, he has co-authored numerous influential papers in international journals, exploring dynamic responses of domes, cylindrical and conical shells with inhomogeneous elastic cores. His work has earned recognition for its analytical depth and contribution to advancing the field of structural dynamics. According to his Scopus profile, Dr. Kotenko has authored 10 scientific publications, received approximately 16 citations, and holds an h-index of 1, reflecting his active engagement and growing impact in the global research community. At KNUCA, he teaches theoretical mechanics and structural dynamics, supervises postgraduate research, and contributes to academic development through innovative research on layered shell mechanics. His continuing investigations into the stress–strain behaviour and stability of multi-layered systems have practical applications in modern civil and aerospace engineering. Dr. Kotenko’s scholarly contributions, combined with his dedication to education and applied mechanics, establish him as a prominent specialist in the field of dynamic analysis of layered and composite structural shells.

Featured Publications