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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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)

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Featured Publications

Mohammad Imrul Islam | Geospatial and Spatial Statistics | Best Researcher Award

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Mr. Mohammad Imrul Islam | Geospatial and Spatial Statistics | Best Researcher Award

Bangladesh Space Research and Remote Sensing Organization (SPARRSO) | Bangladesh

Mr. Mohammad Imrul Islam is a highly dedicated Remote Sensing Researcher and Senior Scientific Officer (SSO) at the Bangladesh Space Research and Remote Sensing Organization (SPARRSO), where he has been contributing his expertise since 2015. With over a decade of professional experience in Remote Sensing (RS) and Geographic Information System (GIS), his work focuses on environmental monitoring, agriculture, forestry, and water resource management, making him one of the promising scientific minds in Bangladesh’s earth observation community. He holds a Master of Engineering in Remote Sensing and GIS from Beihang University, Beijing, China (GPA 3.76), along with both Master of Science and Bachelor of Science degrees in Geography and Environment from Jahangirnagar University, Bangladesh, with first-class distinction. At SPARRSO, he has successfully led and contributed to several national and institutional projects such as flash flood monitoring in Tanguar Haor, spatio-temporal analysis of fisheries habitats, water quality assessment for inland fisheries, and GIS-based marine fishing zone identification. His research showcases his ability to integrate satellite data with advanced geospatial analytics for sustainable environmental management and disaster resilience. His postgraduate research and pilot studies explored innovative approaches such as retrieving Leaf Area Index (LAI) and analyzing the relationship between Solar-Induced Chlorophyll Fluorescence (SIF) and Gross Primary Production (GPP), reflecting his strong foundation in combining remote sensing models with ecological parameters for vegetation monitoring. Mr. Islam has participated in numerous international training and capacity-building programs organized by ISRO, APSCO, NESAC, Hokkaido University, and the University of Twente (ITC, Netherlands), enhancing his global scientific exposure. His technical expertise covers major geospatial and analytical software including ArcGIS, QGIS, ERDAS Imagine, ENVI, SNAP, and cloud-based tools such as Google Earth Engine, complemented by programming proficiency in Python, R, and MATLAB. Fluent in both English and Bangla, and with a TOEIC score of 805, he demonstrates strong communication and collaboration skills across international platforms. Through his ongoing research on seasonality mapping of surface water and hydrometeorological flood monitoring, he continues to contribute toward global climate resilience and sustainable resource management. Actively engaged on ResearchGate, LinkedIn, and ORCID, Mohammad Imrul Islam inspires emerging geospatial researchers across South Asia. His academic rigor, technical competence, and impactful research contributions make him an exemplary candidate for the Best Researcher Award, recognizing his significant role in advancing earth observation and remote sensing research at both national and international levels.

Profiles: Google Scholar | Orcid | Linked In

Featured Publications

Islam, M. I., Rahman, M. M., & Islam, M. Z. (2025). Comparative analysis of chlorophyll-a retrieval algorithms for inland waterbodies of Bangladesh using Sentinel-2 and Landsat-8 imagery. Discover Geoscience.

Niloy, N. M., Habib, S. M. A., Islam, M. I., Haque, M. M., Shammi, M., & Tareq, S. M. (2023). Distribution, characteristics and fate of fluorescent dissolved organic matter (FDOM) in the Bay of Bengal. Marine Pollution Bulletin.

Islam, M. I., Habib, S. M. A., Haque, S. A. U., Sultana, N., Faisal, B. M. R., Rahman, H., & Sharifee, M. N. H. (2020). Applicability of OCO-2 solar induced chlorophyll fluorescence (SIF) data for the estimation of photosynthetic activity in Bangladesh. Journal of Engineering Science, 11(2), 1–9.

Faisal, B. M. R., Rahman, H., Sharifee, N. H., Sultana, N., Islam, M. I., Habib, S. M. A., & Ahammad, T. (2020). Integrated application of remote sensing and GIS in crop information system: A case study on Aman rice production forecasting using MODIS-NDVI in Bangladesh. AgriEngineering, 2(2), 243–257.

Rahman, M. M., Pramanik, M. A. T., Islam, M. I., & Razia, S. (2019). Mapping mangrove forest change in Nijhum Dwip Island. Journal of Environmental Science and Natural Resources, 11(1–2), 25–32.