Publikationsliste – Institut für Photogrammetrie und GeoInformation

begutachtete Zeitschriftenartikel

Voelsen, M., Rottensteiner, F. & Heipke, C. (2024): Transformer Models for Land Cover Classification with Satellite Image Time Series. PFG–Journal of Photogrammetry, Remote Sensing and Geoinformation Science, Vol. 92, 547-568.
DOI: 10.1007/s41064-024-00299-7
M. Voelsen, J. Schachtschneider, C. Brenner (2021): Classification and Change Detection in Mobile Mapping LiDAR Point Clouds, PFG - Journal of Photogrammetry, Remote Sensing and Geoinformation Science
DOI: 10.1007/s41064-021-00148-x

Voelsen, M.., Lauble, S., Rottensteiner, F. , Heipke, C. (2023): Transformer models for multi-temporal land cover classification using remote sensing images. In: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences X-1/W1-2023, pp. 981–990.
DOI: 10.5194/isprs-annals-X-1-W1-2023-981-2023
Voelsen M., Teimouri M., Rottensteiner F., Heipke C. (2022): Investigating 2D and 3D convolutions for multitemporal land cover classification using remote sensing images. In: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences V-3-2022, pp. 271–279.
DOI: 10.5194/isprs-annals-V-3-2022-271-2022
Wittich D., Rottensteiner F., Voelsen M., Heipke C., Müller S. (2022): Deep learning for the detection of early signs for forest damage based on satellite imagery. In: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences V-2-2022, pp. 307–315.
DOI: 10.5194/isprs-annals-V-2-2022-307-2022
Voelsen M., Lobo Torres D., Queiroz Feitosa R., Rottensteiner F., Heipke C. (2021): Investigations on feature similarity and the impact of training data for land cover classification. In: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences V-3-2021, pp. 181–189.
DOI: https://doi.org/10.5194/isprs-annals-V-3-2021-181-2021

Voelsen M., Bostelmann J., Maas A., Rottensteiner F., Heipke C. (2020): Automatically generated training data for land cover classification with CNNs using Sentinel-2 images. In: Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 767–774.
DOI: 10.5194/isprs-archives-XLIII-B3-2020-767-2020