
M.Sc. Jan Stefanski

Nussallee 15
53115 Bonn
Email:
Function:
Research Interest
- Land use and land cover mapping
- Change detection
- Synergy of optical and SAR data
- Image segmentation
Education
Master of Science, Geodesy and Geoinformation, University of Bonn
Master Thesis: Classification of high resolution TerraSAR-X data of urban areas (in German)
Publications
in press
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. in press.
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 = {in press},
doi = {10.1109/JSTARS.2013.2253089}
}





