Transferability of Machine Learning Models for Crop Classification in Remote Sensing Imagery Using a New Test Methodology: A Study on Phenological, Temporal, and Spatial Influences

dc.contributor.author	Dietrich, Peter
dc.date.accessioned	2025-10-31T09:49:39Z
dc.date.available	2025-10-31T09:49:39Z
dc.date.issued	2024
dc.identifier.issn	2072-4292
dc.identifier.uri	http://hdl.handle.net/10900/171739
dc.language.iso	en	en
dc.publisher	Basel : Mdpi	de_DE
dc.relation.uri	http://dx.doi.org/10.3390/rs16091493
dc.subject.ddc	570	de_DE
dc.subject.ddc	550	de_DE
dc.subject.ddc	600	de_DE
dc.subject.ddc	610	de_DE
dc.title	Transferability of Machine Learning Models for Crop Classification in Remote Sensing Imagery Using a New Test Methodology: A Study on Phenological, Temporal, and Spatial Influences	de_DE
dc.type	Article	de_DE
utue.quellen.id	20241001000000_01713
utue.personen.roh	Hoppe, Hauke
utue.personen.roh	Dietrich, Peter
utue.personen.roh	Marzahn, Philip
utue.personen.roh	Weiss, Thomas
utue.personen.roh	Nitzsche, Christian
utue.personen.roh	von Lukas, Uwe Freiherr
utue.personen.roh	Wengerek, Thomas
utue.personen.roh	Borg, Erik
dcterms.isPartOf.ZSTitelID	Remote Sensing	de_DE
dcterms.isPartOf.ZS-Issue	9	de_DE
dcterms.isPartOf.ZS-Volume	16	de_DE
utue.fakultaet	07 Mathematisch-Naturwissenschaftliche Fakultät