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Classifier Directed Data Hybridization for Geographic Sample Supervised Segment Generation

Fourie, Christoffel Ettienne and Schöpfer, Elisabeth (2014) Classifier Directed Data Hybridization for Geographic Sample Supervised Segment Generation. Remote Sensing, 6, pp. 11852-11882. Multidisciplinary Digital Publishing Institute (MDPI). DOI: 0.3390/rs61211852 ISSN 2072-4292

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Official URL: http://www.mdpi.com/journal/remotesensing


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.

Item URL in elib:https://elib.dlr.de/93087/
Document Type:Article
Title:Classifier Directed Data Hybridization for Geographic Sample Supervised Segment Generation
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Fourie, Christoffel EttienneChristoffel.Fourie (at) gmail.comUNSPECIFIED
Schöpfer, Elisabethelisabeth.schoepfer (at) dlr.deUNSPECIFIED
Date:28 November 2014
Journal or Publication Title:Remote Sensing
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In ISI Web of Science:Yes
DOI :0.3390/rs61211852
Page Range:pp. 11852-11882
Publisher:Multidisciplinary Digital Publishing Institute (MDPI)
Keywords:geographic object-based image analysis; segmentation; classification; sample supervised; spatial metrics; metaheuristics
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Erdbeobachtung
DLR - Research theme (Project):R - Vorhaben Zivile Kriseninformation und Georisiken (old)
Location: Oberpfaffenhofen
Institutes and Institutions:German Remote Sensing Data Center > Geo Risks and Civil Security
Deposited By: Schöpfer, Dr. Elisabeth
Deposited On:04 Dec 2014 14:40
Last Modified:14 Dec 2019 04:26

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