Põllukultuuride tuvastamise masinõppe mudeli tunnuste olulisuse hindamine

Järveoja, Mihkel

Põllukultuuride tuvastamise masinõppe mudeli tunnuste olulisuse hindamine

dc.contributor.advisor	Voormansik, Kaupo, juhendaja
dc.contributor.advisor	Matiisen, Tambet, juhendaja
dc.contributor.author	Järveoja, Mihkel
dc.contributor.other	Tartu Ülikool. Loodus- ja täppisteaduste valdkond	et
dc.contributor.other	Tartu Ülikool. Arvutiteaduse instituut	et
dc.date.accessioned	2023-09-14T08:43:49Z
dc.date.available	2023-09-14T08:43:49Z
dc.date.issued	2021
dc.description.abstract	Remotely sensed, in particular satellite data, is already widely used in agricultural parcels monitoring, and this trend is not showing signs of diminishing. Wide range of machine learning algorithms have significantly reduced the burden to interpret bulky and often complex satellite data, contributing to the exploration of new use-cases and services. In this study Random Forest classification model is used to separate 28 crop type classes in Estonia. Input data consisted of two seasons (2018, 2019) of Estonian agricultural parcels and features calculated from Sentinel-1 and Sentinel-2 satellite images, meteorological records and soil maps. Achieved multiclass weighted F1 score for year 2018 test set was 0.82 and for year 2019 0.85. Among most important features were Sentinel-1 VH and VV polarization back-scatter intensities and Sentinel-2 PSRI, NDVI and TC-vegetation indices. It was discovered that Sentinel-2 features were more prominent in early (May) and late season (August), but during mid-season (June, July) their importance decreased significantly. Sentinel-1 back-scatter features were more important during mid-season. It was concluded, that using both radar and optical satellite data ensure better classification result than using any of them separately, since they complement each other.	et
dc.identifier.uri	https://hdl.handle.net/10062/92186
dc.language.iso	est	et
dc.publisher	Tartu Ülikool	et
dc.rights	openAccess	et
dc.rights	Attribution-NonCommercial-NoDerivatives 4.0 International	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/4.0/	*
dc.subject	Põllukultuuride automaatne tuvastamine	et
dc.subject	masinõpe	et
dc.subject	juhumets	et
dc.subject	tunnuste olulisus	et
dc.subject	Sentinel-1	et
dc.subject	Sentinel-2	et
dc.subject.other	magistritööd	et
dc.subject.other	informaatika	et
dc.subject.other	infotehnoloogia	et
dc.subject.other	informatics	et
dc.subject.other	infotechnology	et
dc.title	Põllukultuuride tuvastamise masinõppe mudeli tunnuste olulisuse hindamine	et
dc.type	Thesis	et