Sebastian Böck
University of Natural Resources and Life Sciences, Vienna
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Featured researches published by Sebastian Böck.
Remote Sensing | 2017
Sebastian Böck; Markus Immitzer; Clement Atzberger
Image segmentation is a crucial stage at the very beginning of many geographic object-based image analysis (GEOBIA) workflows. While segmentation quality is generally deemed of great importance, selecting adequate tuning parameters for a segmentation algorithm can be tedious and subjective. Procedures to automatically choose parameters of a segmentation algorithm are meant to make the process objective and reproducible. One of those approaches, and perhaps the most frequently used unsupervised parameter optimization method in the context of GEOBIA is called the objective function, also known as Global Score. Unfortunately, the method exhibits a hitherto widely neglected, yet severe source of instability, which makes quality rankings inconsistent. We demonstrate the issue in detail and propose a modification of the Global Score to mitigate the problem. This hopefully serves as a starting point to spark further development of the popular approach.
GEOBIA 2016 : Solutions and Synergies | 2016
Clement Atzberger; Markus Immitzer; Sebastian Böck; Bruno Schultz; Francesco Vuolo
Only well-chosen segmentation parameters ensure optimum results of object-based image classifications. Manually defining suitable parameter sets can be a time-consuming approach, not necessarily leading to optimum results. Moreover, the manual approach is obviously subjective. An automated evaluation approach is needed to reduce human intervention and to provide objective criteria for ranking different parameter sets. In this work, we test three different ways to find the optimum segmentation. (i) We used a supervised approach integrating the segmentation and classification tasks. The segmentation is optimized directly with respect to the overall accuracy of the subsequent classification. (ii) Using the global score value considering withinand between segment heterogeneity, we run an unsupervised segmentation optimization to find the best segmentation parameters. (iii) Using manually delineated objects we calculate discrepancy measurements like euclidean distance between automatic generated objects and the manual delineated objects in a supervised segmentation evaluation. For all approaches, we present fully autonomous workflows for supervised and unsupervised object-based classification, combining image segmentation, segmentation evaluation and image classification. Starting from a fixed set of randomly selected and manually delineated training samples, suitable segmentation parameters are automatically identified. Finally, we compare the results of the three different approaches. ∗Corresponding author
GEOBIA 2016 : Solutions and Synergies | 2016
Sebastian Böck; Markus Immitzer; Clement Atzberger
Object-based image analysis (OBIA) using high spatial resolution remote sensing imagery has been successfully applied in numerous land-use/land-cover studies. OBIA usually requires user inputs at several stages, limiting reproducibility and automation. This work focuses on developing the potential of a relatively simple and general solution to classification of very-high resolution airborne remote sensing imagery. The approach is demonstrated on the datasets made available for the ISPRS 2D Semantic Labeling Challenge and using solely freely available open-source software. In the first step, Large-Scale Mean-Shift Segmentation (LSMS) is used to obtain an initial partitioning of the test images, using the spectral component as the only input to the algorithm. On the output of LSMS, a in-house implementation of Spectral Difference Segmentation (SDS) is performed. Hundreds of candidate segmentation results are produced over a range of scales, by altering input parameters of the segmentation algorithms. A modified version of a well-known unsupervised segmentation evaluation method based on a combination of global inter-segment and intra- segment heterogeneity is employed to objectively rank and select potentially superior segmentation results for subsequent random forest (RF) classification. Statistical features are calculated for each segment based on its pixel values, using spectral (visible and NIR), height (nDSM) and textural (wavelet) data as input. RF classifiers are trained using subsets of the reference data available and their performance is assessed using held-out test data for validation. Results demonstrate the potential of unsupervised segmentation evaluation to reduce user intervention in OBIA and the ability of freely available software to produce high-quality classification results.
Forest Ecology and Management | 2016
Markus Immitzer; Christoph Stepper; Sebastian Böck; Christoph Straub; Clement Atzberger
International Journal of Applied Earth Observation and Geoinformation | 2016
Wai-Tim Ng; Michele Meroni; Markus Immitzer; Sebastian Böck; Ugo Leonardi; Felix Rembold; Hussein Gadain; Clement Atzberger
Forests | 2017
Kathrin Einzmann; Markus Immitzer; Sebastian Böck; Oliver Bauer; Andreas Schmitt; Clement Atzberger
Remote Sensing of Environment | 2018
Markus Immitzer; Sebastian Böck; Kathrin Einzmann; Francesco Vuolo; Nicole Pinnel; Adelheid Wallner; Clement Atzberger
Archive | 2015
Markus Immitzer; Clement Atzberger; Kathrin Einzmann; Sebastian Böck; Matteo Mattiuzzi; Adelheid Wallner; Rudi Seitz; Nicole Pinnel; Andreas Müller; Matthias Frost
Archive | 2015
Markus Immitzer; Clement Atzberger; Kathrin Einzmann; Sebastian Böck; Matteo Mattiuzzi; Adelheid Wallner; Rudolf Seitz; Nicole Pinnel; Andreas Müller; Matthias Frost
Archive | 2015
Markus Immitzer; Clement Atzberger; Kathrin Einzmann; Sebastian Böck; Matteo Mattiuzzi; Wai Tim Ng; Nicole Pinnel; Andreas Müller; Adelheid Wallner; Rudolf Seitz