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EOLib: An Image Information Mining Project

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

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


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.

Item URL in elib:https://elib.dlr.de/108027/
Document Type:Conference or Workshop Item (Speech)
Title:EOLib: An Image Information Mining Project
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Datcu, Mihaimihai.datcu (at) dlr.deUNSPECIFIED
Espinoza-Molina, Danieladaniela.espinozamolina (at) dlr.deUNSPECIFIED
Dumitru, Corneliu Octaviancorneliu.dumitru (at) dlr.deUNSPECIFIED
Schwarz, Gottfriedgottfried.schwarz (at) dlr.deUNSPECIFIED
Reck, Christophchristoph.reck (at) dlr.deUNSPECIFIED
Manilici, Vladvlad.manilici (at) dlr.deUNSPECIFIED
Date:12 September 2016
Refereed publication:No
Open Access:No
Gold Open Access:No
In ISI Web of Science:No
Keywords:EOLib, SAR
Event Title:EO Open Science 2016
Event Location:Frascati, Italy
Event Type:international Conference
Event Dates:12-14 Sep 2016
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 hochauflösende Fernerkundungsverfahren
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > Photogrammetry and Image Analysis
Deposited By: Dumitru, Corneliu Octavian
Deposited On:18 Nov 2016 11:25
Last Modified:24 May 2017 13:15

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