Hind Hallabia | Signal and Image Processing | Best Researcher Award
Dr. Hind Hallabia at National Institute and School of Applied Sciences of Centre Loire Valley | France
Hind Hallabia is a researcher and educator in Electrical Engineering with expertise in signal and image processing. She has served as a teaching and research assistant in several universities in France and Tunisia, contributing to courses in telecommunications, multimedia, electronics, and digital systems. Her academic journey reflects strong interdisciplinary skills, combining engineering education with advanced training in signal and image analysis. She has actively engaged in applied research, particularly in pansharpening, segmentation, and remote sensing technologies, while also mentoring students in technical subjects. With both research and teaching experience, she continues to bridge innovation and education in engineering.
Professional Profile
Education
Hind Hallabia earned a Ph.D. in Electrical Engineering, specializing in signal and image processing, from the University of Sfax in collaboration with the National Graduate School of Engineering of Sfax. Her doctoral thesis focused on pansharpening techniques using sensor-adjusted filter banks for automatic object detection. She also holds a Master of Science in Electronics, with research conducted at the French Alternative Energies and Atomic Energy Commission and ENIS, centered on optimizing segmentation of primate brain data. Additionally, she earned an Engineering Diploma in Electrical Engineering with a focus on computer systems, and her earlier studies were rooted in experimental sciences.
Experience
She has worked extensively as a teaching and research assistant at institutions in France and Tunisia, including Aix-Marseille University, the University Institute of Technology of Blois, the University Institute of Technology of Aix-Marseille Arles, and the National Graduate Schools of Electronics, Telecommunications, and Engineering of Sfax. Her teaching spans a wide range of subjects such as signals and systems, digital and analog processing, telecommunications, web integration, information theory, operating systems, and cybersecurity. Alongside teaching, she has actively contributed to research laboratories in applied signal and image processing, remote sensing, and computational modeling.
Professional Development
Hind Hallabia has consistently strengthened her professional development through diverse academic and research engagements across Tunisia and France. She has designed and delivered lectures, tutorials, and practical sessions in electronics, telecommunications, multimedia, and digital systems. Her professional growth is further enriched by postdoctoral and collaborative research on pansharpening methods, deep learning, segmentation, and remote sensing applications. She has acquired technical expertise in MATLAB, Python, C++, GDAL, and specialized image processing toolkits. By combining advanced research with higher education teaching, she has cultivated a balanced academic profile that integrates innovation, applied knowledge, and pedagogical excellence to support both students and research communities.
Research Interests
Hind Hallabia’s research is focused on signal and image processing, with a strong specialization in pansharpening, segmentation, and remote sensing technologies. She has developed novel approaches to improve image resolution, quality, and spectral analysis, applying methods such as superpixel segmentation, polynomial regression modeling, and adaptive detail injection. Her work also extends to biomedical image processing, where she applied segmentation techniques to brain imaging and cardiac scintigraphy. More recently, she has explored LiDAR technology, oceanographic data analysis, and deep learning applications for satellite imaging. Her research bridges engineering, artificial intelligence, and environmental sciences, advancing both theoretical and applied methods.
Awards and Recognitions
Hind Hallabia has been recognized through her successful collaborations with international research institutions such as the French Alternative Energies and Atomic Energy Commission, Aix-Marseille University, and the Digital Research Center of Sfax. Her doctoral and postdoctoral contributions in pansharpening and segmentation have been acknowledged within academic and applied research communities. She has also gained recognition by completing advanced training validated by NASA’s Applied Remote Sensing Training Program. Her continuous participation in cross-disciplinary projects and teaching assignments further reflects the professional trust placed in her expertise, marking her as a promising researcher and educator in the field of signal and image processing.
Top Noted Publications
Title: A Graph-Based Superpixel Segmentation Approach Applied to Pansharpening
Year: 2025
Title: Land and aquatic spectral signatures analysis over a spatio-temporal hazardous area acquired by Worldview satellite
Year: 2025
Title: Advanced Trends in Optical Remotely Sensed Data Fusion: Pansharpening Case Study
Year: 2025
Title: A novel detail injection framework using latent low-rank decomposition for multispectral pan-sharpening
Year: 2023
Title: A Graph-Based Textural Superpixel Segmentation Method for Pansharpening Application
Year: 2021
Title: An Optimal Use of SCE-UA Method Cooperated With Superpixel Segmentation for Pansharpening
Year: 2021
Title: An Enhanced Pansharpening Approach Based on Second-Order Polynomial Regression
Year: 2021
Title: A context-driven pansharpening method using superpixel based texture analysis
Year: 2021