Support vector regression and synthetically mixed training data for quantifying urban land cover. Okujeni, A., van der Linden, S., Tits, L., Somers, B., & Hostert, P. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10, 1640-1650. Ensemble learning from synthetically mixed training data for quantifying urban land cover with support vector regression. Okujeni, A., van der Linden, S., Suess, S., & Hostert, P. Remote Sensing of Environment, 216, 482-496.

the colored fluid lines reappear by unmixing after the same number of. Generalizing machine learning regression models using multi-site spectral libraries for mapping vegetation-impervious-soil fractions across multiple cities. One or more lines of colored fluid are also injected into this fluid reservoir. Okujeni, A., Canters, F., Cooper, S.D., Degerickx, J., Heiden, U., Hostert, P., Priem, F., Roberts, D.A., Somers, B., & van der Linden, S. International Journal of Applied Earth Observation and Geoinformation, 78, 295-305. Comparing map-based and library-based training approaches for urban land-cover fraction mapping from Sentinel-2 imagery. Priem, F., Okujeni, A., van der Linden, S., & Canters, F.

Remote Sensing of Environment, 240, 111691. A generalized regression-based unmixing model for mapping forest cover fractions throughout three decades of Landsat data. systems offer powerful spectral unmixing capability to remove autofluorescence, thereby dramatically. Senf, C., Laštovička, J., Okujeni, A., Heurich, M., & van der Linden, S. 2 Human Perception of Light Intensity and of Color. Multi-season unmixing of vegetation class fractions across diverse Californian ecoregions using simulated spaceborne imaging spectroscopy data. Okujeni, A., Jänicke, C., Cooper, S., Frantz, D., Hostert, P., Clark, M., Segl, K., & van der Linden, S. The functionality is also available in the python package 👉 HUB-Workflow and integrated into the 👉 FORCE processing engine. The approach is implemented in the 👉 EnMAP-Box and documented for 👉 E-Learning purposes. The approach has become a core method in several research projects and publications. The development of a machine learning unmixing method based on synthetic training data from spectral databases represents one of my key achievements. Enhancing the transferability and generalization capabilities of such workflows across space, time or sensors constitutes an important step toward their operational implementation. A specific focus is on the integration of sophisticated model training strategies and powerful machine learning methods into big data workflows. This part of my research focusses on exploring and developing innovative remote sensing approaches for applied research.

Unmixing color machine