Dr. Haojin Tang, Guangzhou University, China.
🧑🔬 Dr. Haojin Tang is a Lecturer at Guangzhou University, specializing in 🌐 Artificial Intelligence, Deep Learning, and 🛰️ Hyperspectral Image Processing. He holds a Ph.D. in Information and Communication Engineering and has published 20+ top-tier papers, secured national patents 🧾, and led major research projects. As an inspiring mentor, he guides students to achieve excellence in intelligent manufacturing and environmental sensing. His work is shaping the future of smart technologies and remote sensing innovation. 🚀📡
Profile
Scopus Profile
Orcid Profile
Google Scholar Profile
🎓 Early Academic Pursuits
Dr. Haojin Tang’s academic excellence began at Shenzhen University, where he pursued a B.S. in Electronic Information Engineering (2014–2018) and was recommended for postgraduate study without examination. He later earned both his M.S. (2018–2020) and Ph.D. (2020–2023) in Information and Communication Engineering, supported by the National Scholarship and recognized among the Top 10 Doctoral Dissertations. His solid academic foundation laid the groundwork for a promising research career in artificial intelligence and remote sensing. 🎓📘
💼 Professional Endeavors
Since July 2023, Dr. Tang has served as a Lecturer at the School of Electronic and Communication Engineering, Guangzhou University. He actively mentors undergraduate and graduate students, encouraging them to explore cutting-edge AI techniques in agricultural, forestry, and intelligent manufacturing applications. Under his supervision, students have secured high-impact publications and received numerous provincial and university-level gold awards. 🏅📚
Dr. Tang’s research is rooted in the integration of Artificial Intelligence, Deep Learning, and Hyperspectral Image Processing, with special attention to industrial fault detection and few-shot learning. His contributions include:
-
Publishing over 20 papers in top-tier journals (JCR Q1, CAS TOP) and CCF Class A conferences.
-
Developing innovative algorithms for hyperspectral image classification and zero-shot learning.
-
Leading projects on cross-domain image classification using large language models. 🧠🛰️
🌍 Impact and Influence
Dr. Tang’s influence extends across academia and industry:
-
He has been invited to review for top journals including IEEE TGRS, Remote Sensing, and J-STARS.
-
His interdisciplinary research addresses real-world challenges in environmental monitoring and intelligent manufacturing.
-
His work has contributed to the advancement of UAV-based hyperspectral sensing and fault detection systems. 📡🌱
🧠 Research Skills
Dr. Tang is adept at designing and implementing deep learning architectures for low-shot learning tasks, developing cross-domain classification algorithms, and leveraging large language models for image interpretation. His skills extend to UAV-based remote sensing systems, software development for big data analysis, and interdisciplinary innovation, making him a versatile researcher and practitioner. 🤖💻
🏅 Awards and Honors
-
National Scholarship (Master’s & Ph.D.)
-
Top 10 Doctoral Dissertations at Shenzhen University
-
Student mentees under his guidance have won numerous provincial and institutional gold medals for research excellence.
These accolades underscore his academic distinction and mentorship capabilities. 🎖️🌟
🏛️ Legacy and Future Contributions
Dr. Tang is on a trajectory to become a leading innovator in AI-driven remote sensing and industrial diagnostics. His upcoming work on Large Language Model-driven image classification signals a bold move toward integrating generative AI into remote sensing. As a mentor and researcher, he is nurturing future scientists while paving the way for interpretable and scalable AI models in hyperspectral imaging and intelligent manufacturing. 🚀🌐
Publications Top Notes
-
🛰️ A Spatial–Spectral Prototypical Network for Hyperspectral Remote Sensing Image
Journal: IEEE Geoscience and Remote Sensing Letters
Citations: 64
Year: 2019
✨ Pioneer in spatial-spectral modeling for remote sensing
-
🔍 Multidimensional Local Binary Pattern for Hyperspectral Image Classification
Journal: IEEE Transactions on Geoscience and Remote Sensing
Citations: 37
Year: 2021
🔬 Robust feature extraction in HSI
-
🧠 Fusion of Multidimensional CNN and Handcrafted Features for Small-Sample Hyperspectral Image Classification
Journal: Remote Sensing
Citations: 13
Year: 2022
🤖 Hybrid deep learning for limited data
-
📊 A Multiscale Spatial–Spectral Prototypical Network for Hyperspectral Image Few-Shot Classification
Journal: IEEE Geoscience and Remote Sensing Letters
Citations: 13
Year: 2022
🔁 Improved generalization with few-shot learning
-
⚙️ HFC-SST: Improved Spatial-Spectral Transformer for Hyperspectral Few-Shot Classification
Journal: Journal of Applied Remote Sensing
Citations: 12
Year: 2023
🧭 Enhanced transformer model in HSI
-
🛠️ Multi-Label Zero-Shot Learning for Industrial Fault Diagnosis
Conference: 6th Int’l Conf. on Information Communication and Signal Processing
Citations: 7
Year: 2023
🏭 AI for smart industry diagnostics
-
🛰️ Multi-Scale Attention Adaptive Network for Object Detection in Remote Sensing Images
Conference: 5th Int’l Conf. on Information Communication and Signal Processing
Citations: 4
Year: 2022
🎯 Precision object detection framework
-
🧠 Global-Local Attention-Aware Zero-Shot Learning for Industrial Fault Diagnosis
Journal: IEEE Transactions on Instrumentation and Measurement
Citations: 2
Year: 2025
💡 Breakthrough in industrial ZSL
-
📐 TSSLBP: Tensor-Based Spatial–Spectral Local Binary Pattern
Journal: Journal of Applied Remote Sensing
Citations: 2
Year: 2020
🧮 Tensor-based HSI analysis
-
🧬 AMHFN: Aggregation Multi-Hierarchical Feature Network for Hyperspectral Image Classification
Journal: Remote Sensing
Citations: 1
Year: 2024
🔗 Deep feature aggregation strategy
-
🎯 Dense Convolution Siamese Network for Hyperspectral Image Target Detection
Conference: 5th Int’l Conf. on Information Communication and Signal Processing
Citations: 1
Year: 2022
🛸 High-precision target detection