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Remote Sens. 2014, 6(12), 11852-11882; doi:10.3390/rs61211852

Classifier Directed Data Hybridization for Geographic Sample Supervised Segment Generation

German Remote Sensing Data Center (DFD), German Aerospace Center (DLR), 82234 Oberpfaffenhofen, Germany
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Received: 20 August 2014 / Revised: 11 November 2014 / Accepted: 18 November 2014 / Published: 28 November 2014
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Abstract

Quality segment generation is a well-known challenge and research objective within Geographic Object-based Image Analysis (GEOBIA). Although methodological avenues within GEOBIA are diverse, segmentation commonly plays a central role in most approaches, influencing and being influenced by surrounding processes. A general approach using supervised quality measures, specifically user provided reference segments, suggest casting the parameters of a given segmentation algorithm as a multidimensional search problem. In such a sample supervised segment generation approach, spatial metrics observing the user provided reference segments may drive the search process. The search is commonly performed by metaheuristics. A novel sample supervised segment generation approach is presented in this work, where the spectral content of provided reference segments is queried. A one-class classification process using spectral information from inside the provided reference segments is used to generate a probability image, which in turn is employed to direct a hybridization of the original input imagery. Segmentation is performed on such a hybrid image. These processes are adjustable, interdependent and form a part of the search problem. Results are presented detailing the performances of four method variants compared to the generic sample supervised segment generation approach, under various conditions in terms of resultant segment quality, required computing time and search process characteristics. Multiple metrics, metaheuristics and segmentation algorithms are tested with this approach. Using the spectral data contained within user provided reference segments to tailor the output generally improves the results in the investigated problem contexts, but at the expense of additional required computing time. View Full-Text
Keywords: geographic object-based image analysis; segmentation; classification; sample supervised; spatial metrics; metaheuristics geographic object-based image analysis; segmentation; classification; sample supervised; spatial metrics; metaheuristics
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Fourie, C.; Schoepfer, E. Classifier Directed Data Hybridization for Geographic Sample Supervised Segment Generation. Remote Sens. 2014, 6, 11852-11882.

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