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Object-based Morphological Profiles for Classification of Remote Sensing Imagery

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

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

Kurzfassung

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).

elib-URL des Eintrags:https://elib.dlr.de/106276/
Dokumentart:Zeitschriftenbeitrag
Titel:Object-based Morphological Profiles for Classification of Remote Sensing Imagery
Autoren:
AutorenInstitution oder E-Mail-AdresseAutoren-ORCID-iDORCID Put Code
Geiß, ChristianChristian.Geiss (at) dlr.dehttps://orcid.org/0000-0002-7961-8553NICHT SPEZIFIZIERT
Klotz, MartinMartin.Klotz (at) dlr.deNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Schmitt, AndreasAndreas.Schmitt (at) dlr.deNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Taubenböck, Hanneshannes.taubenboeck (at) dlr.dehttps://orcid.org/0000-0003-4360-9126NICHT SPEZIFIZIERT
Datum:Oktober 2016
Erschienen in:IEEE Transactions on Geoscience and Remote Sensing
Referierte Publikation:Ja
Open Access:Ja
Gold Open Access:Nein
In SCOPUS:Ja
In ISI Web of Science:Ja
Band:54
DOI:10.1109/TGRS.2016.2576978
Seitenbereich:Seiten 5952-5963
Verlag:IEEE - Institute of Electrical and Electronics Engineers
ISSN:0196-2892
Status:veröffentlicht
Stichwörter:Land use/land cover (LULC) classification, mathematical morphology, morphological profiles (MPs), objectbased image analysis (OBIA), supervised classification, very high resolution imagery.
HGF - Forschungsbereich:Luftfahrt, Raumfahrt und Verkehr
HGF - Programm:Raumfahrt
HGF - Programmthema:Erdbeobachtung
DLR - Schwerpunkt:Raumfahrt
DLR - Forschungsgebiet:R EO - Erdbeobachtung
DLR - Teilgebiet (Projekt, Vorhaben):R - Vorhaben Zivile Kriseninformation und Georisiken (alt)
Standort: Oberpfaffenhofen
Institute & Einrichtungen:Deutsches Fernerkundungsdatenzentrum > Georisiken und zivile Sicherheit
Deutsches Fernerkundungsdatenzentrum > Landoberfläche
Hinterlegt von: Geiß, Christian
Hinterlegt am:12 Okt 2016 10:33
Letzte Änderung:30 Jan 2024 12:47

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