Hind Hallabia | Data Science | Excellence in Research Award

Published on by Math Scientist

Dr. Hind Hallabia | Data Science | Excellence in Research Award

Teaching and Researcher Assistant | University Institute of Technology of Saint Etienne Jean Monnet University | France

Dr. Hind Hallabia, affiliated with the University Institute of Technology of Saint-Étienne at Jean Monnet University, France, specializes in remote sensing, pansharpening, and advanced image processing techniques for satellite data analysis. Her research focuses on developing graph-based segmentation methods, superpixel modeling, and data fusion frameworks to enhance multispectral and panchromatic imagery. Dr. Hallabia investigates latent low-rank decomposition, detail-injection mechanisms, and texture-based segmentation models to improve image quality, spatial–spectral fidelity, and analytic accuracy in Earth observation applications. Her work contributes to advances in hazardous-area monitoring, environmental assessment, and optical remote sensing technologies through methodological innovation, algorithm design, and computational enhancements.

Profiles: Scopus | Orcid

Featured Publications

  • Hallabia, H. (2025). A graph-based superpixel segmentation approach applied to pansharpening. Sensors, 25(16), Article 4992. https://doi.org/10.3390/s25164992
    Year: 2025

  • Hallabia, H. (2025). Land and aquatic spectral signatures analysis over a spatio-temporal hazardous area acquired by Worldview satellite. Annual International Congress on Electrical Engineering 2025.
    Year: 2025

  • Hallabia, H. (2025). Advanced trends in optical remotely sensed data fusion: Pansharpening case study. Iris Journal of Astronomy and Satellite Communications.
    Year: 2025

  • Hallabia, H., Hamam, H., & Ben Hamida, A. (2023). A novel detail injection framework using latent low-rank decomposition for multispectral pan-sharpening. Multimedia Tools and Applications, 82, 11971–11995. https://doi.org/10.1007/s11042-022-12770-x
    Year: 2023

  • Hallabia, H., & Hamam, H. (2021). A graph-based textural superpixel segmentation method for pansharpening application. Proceedings of IGARSS 2021. https://doi.org/10.1109/igarss47720.2021.9553304
    Year: 2021