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Object-based Morphological Profiles for Classification of Remote Sensing Imagery

Geiß, Christian and Klotz, Martin and Schmitt, Andreas and Taubenböck, Hannes (2016) Object-based Morphological Profiles for Classification of Remote Sensing Imagery. IEEE Transactions on Geoscience and Remote Sensing, 54 (10), pp. 5952-5963. IEEE - Institute of Electrical and Electronics Engineers. DOI: 10.1109/TGRS.2016.2576978 ISSN 0196-2892

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Official URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7497472

Abstract

Morphological operators (MOs) and their enhancements such as morphological profiles (MPs) are subject to a lively scientific contemplation since they are found to be beneficial for, for example, classification of very high spatial resolution panchromatic, multi-, and hyperspectral imagery. They account for spatial structures with differing magnitudes and, thus, provide a comprehensive multilevel description of an image. In this paper, we introduce the concept of object-based MPs (OMPs) to also encode shape-related, topological, and hierarchical properties of image objects in an exhaustive way. Thereby, we seek to benefit from the so-called object-based image analysis framework by partitioning the original image into objects with a segmentation algorithm on multiple scales. The obtained spatial entities (i.e., objects) are used to aggregate multiple sequences obtained with MOs according to statistical measures of central tendency. This strategy is followed to simultaneously preserve and characterize shape properties of objects and enable both the topological and hierarchical decompositions of an image with respect to the progressive application of MOs. Subsequently, supervised classification models are learned by considering this additionally encoded information. Experimental results are obtained with a random forest classifier with heuristically tuned hyperparameters and a wrapper-based feature selection scheme. We evaluated the results for two test sites of panchromatic WorldView-II imagery, which was acquired over an urban environment. In this setting, the proposed OMPs allow for significant improvements with respect to classification accuracy compared to standard MPs (i.e., obtained by paired sequences of erosion, dilation, opening, closing, opening by top-hat, and closing by top-hat operations).

Item URL in elib:https://elib.dlr.de/106276/
Document Type:Article
Title:Object-based Morphological Profiles for Classification of Remote Sensing Imagery
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Geiß, Christianchristian.geiss (at) dlr.deUNSPECIFIED
Klotz, MartinMartin.Klotz (at) dlr.deUNSPECIFIED
Schmitt, AndreasAndreas.Schmitt (at) dlr.deUNSPECIFIED
Taubenböck, Hanneshannes.taubenboeck (at) dlr.deUNSPECIFIED
Date:October 2016
Journal or Publication Title:IEEE Transactions on Geoscience and Remote Sensing
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:54
DOI :10.1109/TGRS.2016.2576978
Page Range:pp. 5952-5963
Publisher:IEEE - Institute of Electrical and Electronics Engineers
ISSN:0196-2892
Status:Published
Keywords:Land use/land cover (LULC) classification, mathematical morphology, morphological profiles (MPs), objectbased image analysis (OBIA), supervised classification, very high resolution imagery.
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
German Remote Sensing Data Center > Land Surface
Deposited By: Geiß, Christian
Deposited On:12 Oct 2016 10:33
Last Modified:08 Mar 2018 18:34

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