Implementing image processing workflows

Research
My research contributes to the implementation of innovative and effective remote sensing workflows to address real-world application challenges. A specific focus is on the integration of sophisticated model training strategies and powerful machine learning methods into big data workflows. Enhancing the transferability and generalization capabilities of such workflows across space, time, and/or sensors constitutes an important step toward their operational implementation.

The development of a machine learning unmixing method based on synthetic training data from spectral databases represents one of my key achievements. The approach has become a core method in several research projects and publications. The approach is implemented in the 👉EnMAP-Box and documented for 👉E-Learning purposes. The functionality is also available in the python package 👉HUB-Workflow and integrated into the 👉FORCE processing engine.

Selected publications

• Okujeni, A., Kowalski, K., Lewińska, K.E., Schneidereit, S., & Hostert, P. (2024). Multidecadal grassland fractional cover time series retrieval for Germany from the Landsat and Sentinel-2 archives. Remote Sensing of Environment, 302, 113980. https://doi.org/10.1016/j.rse.2023.113980

• Kowalski, K., Okujeni, A., & Hostert, P. (2023). A generalized framework for drought monitoring across Central European grassland gradients with Sentinel-2 time series. Remote Sensing of Environment 286, 113449. https://doi.org/10.1016/j.rse.2022.113449

• Okujeni, A., van der Linden, S., Suess, S., & Hostert, P. (2017). Ensemble learning from synthetically mixed training data for quantifying urban land cover with support vector regression. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10, 1640-1650. https://doi.org/10.1109/JSTARS.2016.2634859

• Okujeni, A., van der Linden, S., Tits, L., Somers, B., & Hostert, P. (2013). Support vector regression and synthetically mixed training data for quantifying urban land cover. Remote Sensing of Environment, 137, 184-197. https://doi.org/10.1016/j.rse.2013.06.0079