English
 
Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
Add to Basket
  Local TagsRelease HistoryDetailsSummary

Released
Journal Article

Maschinelles Lernen bei der Auswertung von Fernerkundungsdaten

Authors
/persons/resource/segl

Segl,  K.
1.4 Remote Sensing, 1.0 Geodesy, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;
Vol. 8, Issue 1 (2018), GFZ Journal 2018, System Erde : GFZ Journal, Deutsches GeoForschungsZentrum;

/persons/resource/nbohn

Bohn,  Niklas
1.4 Remote Sensing, 1.0 Geodesy, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;
Vol. 8, Issue 1 (2018), GFZ Journal 2018, System Erde : GFZ Journal, Deutsches GeoForschungsZentrum;

/persons/resource/chabri

Chabrillat,  S.
1.4 Remote Sensing, 1.0 Geodesy, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;
Vol. 8, Issue 1 (2018), GFZ Journal 2018, System Erde : GFZ Journal, Deutsches GeoForschungsZentrum;

/persons/resource/carstenn

Neumann,  C.
1.4 Remote Sensing, 1.0 Geodesy, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;
Vol. 8, Issue 1 (2018), GFZ Journal 2018, System Erde : GFZ Journal, Deutsches GeoForschungsZentrum;

/persons/resource/roessner

Roessner,  S.
1.4 Remote Sensing, 1.0 Geodesy, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;
Vol. 8, Issue 1 (2018), GFZ Journal 2018, System Erde : GFZ Journal, Deutsches GeoForschungsZentrum;

/persons/resource/ward

Ward,  Kathrin
1.4 Remote Sensing, 1.0 Geodesy, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;
Vol. 8, Issue 1 (2018), GFZ Journal 2018, System Erde : GFZ Journal, Deutsches GeoForschungsZentrum;

/persons/resource/ola

Wolanin,  Aleksandra
1.4 Remote Sensing, 1.0 Geodesy, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;
Vol. 8, Issue 1 (2018), GFZ Journal 2018, System Erde : GFZ Journal, Deutsches GeoForschungsZentrum;

External Ressource
Fulltext (public)

GFZ_syserde.08.01.3.pdf
(Publisher version), 2MB

Supplementary Material (public)
There is no public supplementary material available
Citation

Segl, K., Bohn, N., Chabrillat, S., Neumann, C., Roessner, S., Ward, K., Wolanin, A. (2018): Maschinelles Lernen bei der Auswertung von Fernerkundungsdaten. - System Erde, 8, 1, 18-25.
https://doi.org/10.2312/GFZ.syserde.08.01.3


Cite as: https://gfzpublic.gfz-potsdam.de/pubman/item/item_3539922
Abstract
Remote sensing data analysis retrieves spatial-temporal information about the Earth‘s surface from remotely sensed optical and radar images. For this purpose accurate and efficient classification or parameter quantification techniques must be used. Consequently, there exists a long tradition in remote sensing to employ methods and techniques from the field of machine learning. They can be regarded as „universal function approximators“ that are able to link any data in order to derive connections, conclusions and predictions efficiently using different learning strategies. In the following, current research topics of the Remote Sensing section of the GFZ are presented, in which different forms of machine learning are used.