FB6 Mathematik/Informatik/Physik

Institut für Informatik

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Peer-reviewed journal papers 


Ubben, N., Pukrop, M. Jarmer, T., 2024. Spatial resolution as a factor for efficient UAV-based weed mapping – A soybean field case study. Remote Sensing, 16(10), 1778.

Niemeyer, M., Renz1, M., Pukrop, M., Hagemann, D., Zurheide, T., di Marco, D., Höferlin, M., Stark, P., Rahe, F., Igelbrink, M., Jenz, M., Jarmer, T., Trautz, D., Stiene, S., Hertzberg, J., 2024. Cognitive Weeding: an approach to single‑plant specific weed regulation. Künstliche Intelligenz.

Seiche, A.T., Wittstruck, L., Jarmer, T., 2024. Weed detection from UAV imagery using deep learning – A multispectral sensor comparison between high-end and low-cost. Sensors, 24, 1544.


Pöttker, M., Kiehl, K., Jarmer, T. & Trautz, D., 2023. Convolutional Neural Network maps plant communities in semi-natural grasslands using multispectral Unmanned Aerial Vehicle imagery. Remote Sensing, 15(7), 1945.

Storch, M., de Lange, N., Jarmer, T. & Waske, B., 2023. Detecting historical terrain anomalies with UAV-LiDAR data using spline-approximation and support vector machines. IEEE Journal of Selected Topics in Applied Earth Observation and Remote Sensing, 16, 3158-3173.


Wittstruck, L., Jarmer, T., Trautz, D. & Waske, B., 2022. Estimating LAI from winter wheat using UAV data and CNNs. IEEE Geoscience and Remote Sensing Letters, 19, 1-5 (Art no. 2503405).

Stojanovic, O., Siegmann, B., Jarmer, T., Pipa, G. & Leugering, J., 2022. Bayesian hierarchical models can infer interpretable predictions of leaf area index from heterogeneous datasets. Frontiers in Environmental Science, 9, 780814.

Storch, M., Jarmer, T., Adam, M., de Lange, N., 2022. Systematic approach for remote sensing of historical conflict landscapes with UAV-based Laserscanning. Sensors, 22(1), 217.


Zhang, S. P., Foerster, S., Medeiros, P., de Araujo, J. C., Duan, Z., Bronstert, A., & Waske, B. (2021). Mapping regional surface water volume variation in reservoirs in northeastern Brazil during 2009-2017 using high-resolution satellite images. Science of the Total Environment, 789, https://doi.org/10.1016/j.scitotenv.2021.147711.

Hänel, T., Jarmer, T., & Aschenbruck, N.,2021. Learning a transform base for the multi- to hyperspectral sensor network with K-SVD. Sensors, 21, 7296.

Wittstruck, L., Kühling, I., Trautz, D., Kohlbrecher, M., Jarmer, T., 2021. UAV-based RGB imagery for Hokkaido pumpkin (Cucurbita max.) detection and yield estimation.Sensors, 21, 15p.


Muro, J., Varea, A., Strauch, A., Guelmami, A., Fitoka, E., Thonfeld, F., Diekkrüger, B., & Waske, B. (2020). Multitemporal optical and radar metrics for wetland mapping at national level in Albania. Heliyon, 6, doi.org/10.1016/j.heliyon.2020.e04496

Fenske, K., Feilhauer, H., Förster, M., Stellmes, M., & Waske, B. (2020). Hierarchical classification with subsequent aggregation of heathland habitats using an intra-annual RapidEye time-series. International Journal of Applied Earth Observation and Geoinformation, 87, 102036.

Rosentreter, J., Hagensieker, R., & Waske, B. (2020). Towards large-scale mapping of local climate zones using multitemporal Sentinel 2 data and convolutional neural networks. Remote Sensing of Environment, 237, 111472.


Bauer, J., Jarmer, T., Schittenhelm, S., Siegmann, B. & Aschenbruck, N., 2019. Processing and filtering of leaf area index time series assessed by in-situ wireless sensor networks. Computers and Electronics in Agriculture, 165, 14p.

Crowson, M., Hagensieker, R., & Waske, B., 2019. Mapping land cover change in northern Brazil with limited training data. International Journal of Applied Earth Observation and Geoinformation, 78, 202-214.

Haburaj, V., Krause, J., Pless, S., Waske, B., & Schutt, B., 2019. Evaluating the Potential of Semi-Automated Image Analysis for Delimiting Soil and Sediment Layers. Journal of Field Archaeology, 44(8), 538-549.

Hänel, T., Jarmer, T. & Aschenbruck, N., 2019. Using distributed compressed sensing to derive continuous hyperspectral imaging from a wireless sensor network. Computers and Electronics in Agriculture, 166, 9p.

Marchetti, F., Waske, B., Arbelo, M., Moreno-Ruíz, J. A., & Alonso-Benito, A., 2019. Mapping Chestnut Stands Using Bi-Temporal VHR Data. Remote Sensing, 11(21), 2560.


Baumann, M., Levers, C., Macchi, L., Bluhm, H., Waske, B., Gasparri, N. I., & Kuemmerle, T., 2018. Mapping continuous fields of tree and shrub cover across the Gran Chaco using Landsat 8 and Sentinel-1 data. Remote Sensing of Environment, 216, 201-211.

Hagensieker, R., & Waske, B., 2018. Evaluation of multi-frequency SAR images for tropical land cover mapping. Remote Sensing, 10(2), 1-16.

Kanning, M., Kühling, I., Trautz, D. & Jarmer, T., 2018. High resolution UAV-based hyperspectral imagery for LAI and chlorophyll estimations from wheat for yield prediction. Remote Sensing, 10(12), 2000.

Muro, J., Strauch, A., Heinemann, S., Steinbach, S., Thonfeld, F., Waske, B., & Diekkruger, B., 2018. Land surface temperature trends as indicator of land use changes in wetlands. International Journal of Applied Earth Observation and Geoinformation, 70, 62-71.

Polinova, M., Jarmer, T., Brook, A., 2018. Spectral data source effect on crop state estimation by vegetation indices. Environmental Earth Sciences, 77, 752.

Steinhausen, M. J., Wagner, P. D., Narasimhan, B., & Waske, B., 2018. Combining Sentinel-1 and Sentinel-2 data for improved land use and land cover mapping of monsoon regions. International Journal of Applied Earth Observation and Geoinformation, 73, 595-604.

Zhang, S., Foerster, S., Medeiros, P., de Araújo, J. C., & Waske, B., 2018. Effective water surface mapping in macrophyte-covered reservoirs in NE Brazil based on TerraSAR-X time series. International Journal of Applied Earth Observation and Geoinformation, 69, 41-55.


Hagensieker, R., Roscher, R., Rosentreter, J., Jakimow, B., & Waske, B., 2017. Tropical land use land cover mapping in Pará (Brazil) using discriminative Markov random fields and multi-temporal TerraSAR-X data. International Journal of Applied Earth Observation and Geoinformation, 63, 244-256.

Locher-Krause, K. E., Volk, M., Waske, B., Thonfeld, F., & Lautenbach, S., 2017. Expanding temporal resolution in landscape transformations: Insights from a landsat-based case study in Southern Chile. Ecological Indicators, 75, 132-144.

Mack, B., & Waske, B., 2017. In-depth comparisons of MaxEnt, biased SVM and one-class SVM for one-class classification of remote sensing data. Remote Sensing Letters, 8(3), 290-299.

Rosentreter, J., Hagensieker, R., Okujeni, A., Roscher, R., Wagner, P. D., & Waske, B., 2017. Subpixel mapping of urban areas using EnMAP data and Multioutput Support Vector Regression. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(5), 1938-1948.


Bauer, J., Siegmann, B., Jarmer, T. & Aschenbruck, N., 2016. On the potential of wireless sensor networks for the in-situ assessment of crop leaf area index. Computers and Electronics in Agriculture, 128, 149-159.

Jarmer, T. & Shoshany, M., 2016. Relationships between soil spectral and chemical properties along a climatic gradient in the Judean Desert. Arid Land Research and Management, 30(2), 123-137.

Joshi, N., Baumann, M., Ehammer, A., Fensholt, R., Grogan, K., Hostert, P., Rudbeck Jepsen, M., Kuemmerle, T., Meyfroidt, P., Mitchard, E.T.A., Reiche, J., Ryan, C.M. & Waske, B., 2016. A review of the application of optical and radar remote sensing data fusion to land use mapping and monitoring. Remote Sensing, 8(1), 70.

Kanning, M., Siegmann, B. & Jarmer, T., 2016. Regionalization of uncovered agricultural soils based on organic carbon and soil texture estimations. Remote Sensing, 8, 927-943.

Mack, B., Roscher, R., Stenzel, S., Feilhauer, H., Schmidtlein, S., & Waske, B., 2016. Mapping raised bogs with an iterative one-class classification approach. ISPRS Journal of Photogrammetry and Remote Sensing, 120, 53-64.

Roscher, R., & Waske, B., 2016. Shapelet-based sparse representation for landcover classification of hyperspectral images. Geoscience and Remote Sensing, IEEE Transactions on, 54(3), 1623-1634.

Trinder, J., & Waske, B., 2016. Theme section for 36th International Symposium for Remote Sensing of the Environment in Berlin. ISPRS Journal of Photogrammetry and Remote Sensing, 119, 463-463.

Wagner, P. D., & Waske, B., 2016. Importance of spatially distributed hydrologic variables for land use change modeling. Environmental Modelling & Software, 83, 245-254.

Zhang, S. P., Foerster, S., Medeiros, P., de Araujo, J. C., Motagh, M., & Waske, B., 2016. Bathymetric survey of water reservoirs in north-eastern Brazil based on TanDEM-X satellite data. Science of The Total Environment, 571, 575-593.


Beyer, F., Jarmer, T. & Siegmann, B., 2015. Identification of agricultural crop types in Northern Israel using multitemporal RapidEye data. Photogrammetrie-Fernerkundung-Geoinformation, 1/2015, 21-32.

Du, P., Samat, A., Waske, B., Liu, S., & Li, Z., 2015. Random Forest and Rotation Forest for fully polarized SAR image classification using polarimetric and spatial features. ISPRS Journal of Photogrammetry and Remote Sensing, 105(0), 38-53.

Siegmann, B. & Jarmer, T., 2015. Comparison of different regression models and validation techniques for the assessment of wheat leaf area index from hyperspectral data. International Journal of Remote Sensing, 36(18), 4519-4534.

Siegmann, B., Jarmer, T., Beyer, F. & Ehlers, M., 2015. The potential of pan-sharpened EnMAP data for the assessment of wheat LAI. Remote Sensing, 7, 12737-12762.


Mack, B., Roscher, R., & Waske, B., 2014. Can I trust my one-class classification? Remote Sensing, 6(9), 8779-8802.

Stefanski, J., Chaskovskyy, O., & Waske, B., 2014. Mapping and monitoring of land use changes in post-Soviet western Ukraine using remote sensing data. Applied Geography, 55, 155-164.

Stefanski, J., Kuemmerle, T., Chaskovskyy, O., Griffiths, P., Havryluk, V., Knorn, J., Korol, N., Sieber, A. & Waske, B., 2014. Mapping land management regimes in Western Ukraine using optical and SAR data. Remote Sensing, 6(6), 5279-5305.

Suess, S., van der Linden, S., Leitao, P. J., Okujeni, A., Waske, B., & Hostert, P., 2014. Import Vector Machines for quantitative analysis of hyperspectral data. IEEE Geoscience and Remote Sensing Letters, 11(2), 449-453.


Jarmer, T., 2013. Spectroscopy and hyperspectral imagery for monitoring summer barley. International Journal of Remote Sensing, 34(17), 6067-6078.

Stefanski, J., Mack, B., & Waske, B., 2013. Optimization of object-based image analysis with Random Forests for land cover mapping. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 6(6), 2492-2504.


Klemenjak, S., Waske, B., Valero, S., & Chanussot, J., 2012. Automatic detection of rivers in high-resolution SAR data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 5(5), 1364-1372.

Roscher, R., Forstner, W., & Waske, B., 2012. (IVM)-V-2: Incremental import vector machines. Image and Vision Computing, 30(4-5), 263-278.

Roscher, R., Waske, B., & Forstner, W., 2012. Incremental Import Vector Machines for classifying hyperspectral data. IEEE Transactions on Geoscience and Remote Sensing, 50(9), 3463-3473.

Waske, B., van der Linden, S., Oldenburg, C., Jakimow, B., Rabe, A., & Hostert, P., 2012. imageRF - A user-oriented implementation for remote sensing image analysis with Random Forests. Environmental Modelling & Software, 35, 192-193.


Schwanghart, W. & Jarmer, T., 2011. Linking spatial patterns of soil organic carbon to topography - a case study from south-eastern Spain. Geomorphology, 126, 252-263.

Shoshany, M., Kizel, F., Netanyahu, N.S., Goldshlager, N., Jarmer, T. & Even-Tzur, G., 2011. An iterative search in end-member fraction space for spectral unmixing. Geoscience and Remote Sensing Letters, 99, 706-709.