Identifying suitable segmentation parameters for an object-based image classification

Clement Atzberger*, Markus Immitzer, Sebastian Böck, B. Schultz, F. Vuolo

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

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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 within- and 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.
Original languageEnglish
Title of host publicationProceedings of GEOBIA 2016 : Solutions and synergies, 14-16 September 2016, Enschede, Netherlands
EditorsN. Kerle, M. Gerke, S. Lefevre
Place of PublicationEnschede
PublisherUniversity of Twente, Faculty of Geo-Information Science and Earth Observation (ITC)
Number of pages1
ISBN (Print)978-90-365-4201-2
Publication statusPublished - 14 Sept 2016
Externally publishedYes
Event6th International Conference on Geographic Object-Based Image Analysis, GEOBIA 2016: Solutions & Synergies - University of Twente Faculty of Geo-Information and Earth Observation (ITC), Enschede, Netherlands
Duration: 14 Sept 201616 Sept 2016
Conference number: 6


Conference6th International Conference on Geographic Object-Based Image Analysis, GEOBIA 2016
Abbreviated titleGEOBIA
Internet address


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