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Object-Based Postclassification Relearning

Geiß, Christian and Taubenböck, Hannes (2015) Object-Based Postclassification Relearning. IEEE Geoscience and Remote Sensing Letters, 12 (11), pp. 2336-2340. IEEE - Institute of Electrical and Electronics Engineers. doi: 10.1109/LGRS.2015.2477436. ISSN 1545-598X.

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Official URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7276994


In this letter, we present an object-based postclassification relearning approach for enhanced supervised remote sensing image classification. Conventional postclassification processing techniques aim to enhance the classification accuracy by imposing smoothness priors in the image domain (based on, for example, majority filtering or Markov random fields). In contrast to that, here, a supervised classification model is learned for the second time, with additional information generated from the initial classification outcome to enhance the discriminative properties of relearned decision functions. This idea is followed within an object-based image analysis framework. Therefore, we model spatial-hierarchical context relations with the preliminary classification outcome by computing class-related features using a triplet of hierarchical segmentation levels. Those features are used to enlarge the initial feature space and impose spatial regularization in the relearned model. We evaluate the relevance of the method in the context of classifying of a high-resolution multispectral image, which was acquired over an urban environment. The experimental results show an enhanced classification accuracy using this method compared to both per-pixel-based approach and outcomes obtained with a conventional object-based postclassification processing technique (i.e., object-based voting).

Item URL in elib:https://elib.dlr.de/102275/
Document Type:Article
Title:Object-Based Postclassification Relearning
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Journal or Publication Title:IEEE Geoscience and Remote Sensing Letters
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In ISI Web of Science:Yes
Page Range:pp. 2336-2340
Publisher:IEEE - Institute of Electrical and Electronics Engineers
Keywords:Classification postprocessing (CPP), object-based image analysis (OBIA), relearning, SVM
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Earth Observation
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: Geiß, Christian
Deposited On:18 Jan 2016 11:32
Last Modified:03 Jun 2020 10:53

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