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EOLib: An Image Information Mining Project

Datcu, Mihai und Espinoza-Molina, Daniela und Dumitru, Corneliu Octavian und Schwarz, Gottfried und Reck, Christoph und Manilici, Vlad (2016) EOLib: An Image Information Mining Project. EO Open Science 2016, 2016-09-12 - 2016-09-14, Frascati, Italy.

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Offizielle URL: http://eoopenscience.esa.int/page_session2.php

Kurzfassung

The abundance of available satellite images calls for their automated analysis and interpretation, including the semantic annotation of discovered objects as well as the monitoring of changes within image time series. A common approach is to cut large satellite image into contiguous patches and to classify each patch separately by attaching a semantic patch content label to it. In this context, the selected patch size is a critical parameter, as patches being too large may contain multiple objects and patches being too small may not be understandable due to missing contextual information. This approach has been embedded into an interactive active learning and exploitation environment within the ESA-funded EOLib project. The software of EOLib allows automated image data ingestion, feature extraction, and semantic image content annotation supported by interactive visualization tools. We report about our experiences with medium and high resolution Synthetic Aperture Radar (SAR) and optical multispectral image classification when using such an active learning approach. The most important phenomenon is the impact of image resolution. The higher the resolution, the higher the number of discernible land cover categories, in particular for built-up areas and industrial sites where we can see and interpret the impact of distinct human-made activities. Here, the discernible land cover categories depend on the actual image resolution. This becomes apparent when we compare the same target areas acquired by different space-borne SAR sensors (e.g., Sentinel-1A versus TerraSAR-X). In addition, it turns out that several country-specific regional surface cover categories can be trained and retrieved with SAR images that often appear differently in optical satellite images; however, any increase in classification accuracy has to be paid for by higher computational effort. Thus, EOLib represents an approach for future ground segments whose functionality will no longer be limited to the mere generation of level 1,2, or 3 products, but will include automated and user-friendly image content analysis and annotation.

elib-URL des Eintrags:https://elib.dlr.de/108027/
Dokumentart:Konferenzbeitrag (Vortrag)
Titel:EOLib: An Image Information Mining Project
Autoren:
AutorenInstitution oder E-Mail-AdresseAutoren-ORCID-iDORCID Put Code
Datcu, Mihaimihai.datcu (at) dlr.deNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Espinoza-Molina, Danieladaniela.espinozamolina (at) dlr.deNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Dumitru, Corneliu Octaviancorneliu.dumitru (at) dlr.deNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Schwarz, Gottfriedgottfried.schwarz (at) dlr.deNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Reck, Christophchristoph.reck (at) dlr.dehttps://orcid.org/0000-0002-4300-4920NICHT SPEZIFIZIERT
Manilici, Vladvlad.manilici (at) dlr.deNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Datum:12 September 2016
Referierte Publikation:Nein
Open Access:Nein
Gold Open Access:Nein
In SCOPUS:Nein
In ISI Web of Science:Nein
Status:veröffentlicht
Stichwörter:EOLib, SAR
Veranstaltungstitel:EO Open Science 2016
Veranstaltungsort:Frascati, Italy
Veranstaltungsart:internationale Konferenz
Veranstaltungsbeginn:12 September 2016
Veranstaltungsende:14 September 2016
Veranstalter :ESA
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 hochauflösende Fernerkundungsverfahren (alt)
Standort: Oberpfaffenhofen
Institute & Einrichtungen:Institut für Methodik der Fernerkundung > Photogrammetrie und Bildanalyse
Hinterlegt von: Dumitru, Corneliu Octavian
Hinterlegt am:18 Nov 2016 11:25
Letzte Änderung:24 Apr 2024 20:13

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