Alumnus
Department for Photogrammetry
Nussallee 15
53115 Bonn


Email:


Research Associate (m)

Research Interest

One-class classification for mapping land cover classes with remote sensing imagery.

Derivation of biophysical crop parameters with remote sensing imagery.

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Curriculum Vitae

since 08/2010: Research Assistant, University of Bonn, Institute of Geodesy and Geoinformation (Department of Photogrammetry).

08/2009 – 07/2010: Research Assistant, University of Bonn, Center for Remote Sensing of Land Surfaces.


10/2003 – 07/2009: Graduation in Geography, University of Bonn.

Magister Thesis: "Uncertainty Factors in Field Spectroscopy – a comparative study on the reproducibility of measurments from two spectroradiometer systems." (In German)

09/2002 – 06/2003: Alternative Service, Wilhelm-Hartschen-School, Solingen.

06/2002: Abitur, Störck-Gymnasium, Bad Saulgau.

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Publications

2013

Jan Stefanski and Benjamin Mack and Björn Waske, "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. Vol. 6(6), pp. 2492-2504. 2013.

A prerequisite for object-based image analysis is the generation of adequate segments. However, the parameters for the image segmentation algorithms are often manually defined. Therefore, the generation of an ideal segmentation level is usually costly and user-depended. In this paper a strategy for a semi-automatic optimization of object-based classification of multitemporal data is introduced by using Random Forests (RF) and a novel segmentation algorithm. The Superpixel Contour (SPc) algorithm is used to generate a set of different levels of segmentation, using various combinations of parameters in a user-defined range. Finally, the best parameter combination is selected based on the cross-validation-like out-of-bag (OOB) error that is provided by RF. Therefore, the quality of the parameters and the corresponding segmentation level can be assessed in terms of the classification accuracy, without providing additional independent test data. To evaluate the potential of the proposed concept, we focus on land cover classification of two study areas, using multitemporal RapidEye and SPOT 5 images. A classification that is based on eCognition's widely used Multiresolution Segmentation algorithm (MRS) is used for comparison. Experimental results underline that the two segmentation algorithms SPc and MRS perform similar in terms of accuracy and visual interpretation. The proposed strategy that uses the OOB error for the selection of the ideal segmentation level provides similar classification accuracies, when compared to the results achieved by manual-based image segmentation. Overall, the proposed strategy is operational and easy to handle and thus economizes the findings of optimal segmentation parameters for the Superpixel Contour algorithm.

@article{Stefanski2013Optimization,
  author = {Stefanski, Jan and Mack, Benjamin and Waske, Bj\"orn},
  title = {Optimization of object-based image analysis with Random Forests for land cover mapping},
  journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
  year = {2013},
  volume = {6},
  number = {6},
  pages = {2492--2504},
  doi = {10.1109/JSTARS.2013.2253089}
}

2011

Benjamin Mack and Björn Waske, "Optimizing support vector data description by automatically generated outliers.", In 7th Works. of the EARSeL Special Interest Group Imaging Spectroscopy. 2011.

[none]

@inproceedings{Mack2011Optimizing,
  author = {Mack, Benjamin and Waske, Bj\"orn},
  title = {Optimizing support vector data description by automatically generated outliers.},
  booktitle = {7th Works. of the EARSeL Special Interest Group Imaging Spectroscopy},
  year = {2011}
}
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