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Assessing automatically-detected changes in the post-classification change-detection of Sentinel-2 data with Visual Analytics

Urheber*innen
/persons/resource/sips

Sips,  M.
1.4 Remote Sensing, 1.0 Geodesy, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

/persons/resource/vlavr

Lavrentiev,  Valery
1.4 Remote Sensing, 1.0 Geodesy, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

/persons/resource/eggi

Eggert,  Daniel
1.4 Remote Sensing, 1.0 Geodesy, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

/persons/resource/neel

Neelmeijer,  Julia
1.4 Remote Sensing, 1.0 Geodesy, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

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Zitation

Sips, M., Lavrentiev, V., Eggert, D., Neelmeijer, J. (2020): Assessing automatically-detected changes in the post-classification change-detection of Sentinel-2 data with Visual Analytics - Proceedings, 40. Wissenschaftliche-Technische Jahrestagung der DGPF (Stuttgart 2020).


Zitierlink: https://gfzpublic.gfz-potsdam.de/pubman/item/item_5003203
Zusammenfassung
Satellite remote sensing offers the possibility to monitor the Earth's surface at high temporal and spatial resolutions. An important methodological field is the detection and interpretation of changes on the Earth’s surface. A robust and widely utilized family of approaches is post-classification change-detection (PCCD). In our research, we address an important challenge to using PCCD from a user’s perspective. Users often face difficulties finding changes in the result sets of PCCD that are relevant to their application scenarios. We propose a Visual Analytics approach that supports users in terms of exploring the temporal dynamics and the spatial distribution of automatically-detected changes generated via PCCD.