{"id":{"repo_id":"regina","oai_identifier":"oai:uregina.scholaris.ca:10294/15543"},"canonical_url":"https://search.dev.ndltd.org/etd/regina/oai:uregina.scholaris.ca:10294/15543","repository":{"repo_id":"regina","name":"University of Regina","base_url":"https://uregina.scholaris.ca/server/oai/request"},"display":{"title":"Comparison of Random Forests, Support Vector Machine and Artificial Neural Network Methods for Agriculture Land Cover Classification","abstract":"Land cover classification is critical in remote sensing. Reliable classification on land cover is required to address a wide range of environmental issues. Over recent years, the application of machine learning techniques in remote sensing has attracted wide attention. Machine learning has the capacity to classify land cover in remotely sensed photos effectively and efficiently. Machine learning techniques, such as Random Forests (RF), Support Vector Machines (SVM) and Artificial Neural Networks (ANN) can be applied in land cover classification. However, putting a machine learning categorization system in place is not easy, especially in the field of agricultural land classifications. Very limited research can be found using the machine learning techniques for agriculture land cover classifications. In the Canadian prairies, the cropping systems are based on simplified input-driven production of annual crops, which could affect land cover classification. To investigate the performance of machine learning techniques, the present research is conducted in the southern prairie region of Saskatchewan. The satellite images used for classification are from Sentinel-2, which is a publicly data from the European Space Agency. In total, 133,080 samples were analyzed using stratified random sampling, divided into training (70%) and test (30%) subsets. The accuracy is assessed by a variety of indicators. It is found that RF has the highest overall accuracy while the SVM has the lowest accuracy. The ANN has more advantages compared to others. Future agricultural land cover classifications can be based on the current research.","abstract_html":"Land cover classification is critical in remote sensing. Reliable classification on land cover is required to address a wide range of environmental issues. Over recent years, the application of machine learning techniques in remote sensing has attracted wide attention. Machine learning has the capacity to classify land cover in remotely sensed photos effectively and efficiently. Machine learning techniques, such as Random Forests (RF), Support Vector Machines (SVM) and Artificial Neural Networks (ANN) can be applied in land cover classification. However, putting a machine learning categorization system in place is not easy, especially in the field of agricultural land classifications. Very limited research can be found using the machine learning techniques for agriculture land cover classifications. In the Canadian prairies, the cropping systems are based on simplified input-driven production of annual crops, which could affect land cover classification. To investigate the performance of machine learning techniques, the present research is conducted in the southern prairie region of Saskatchewan. The satellite images used for classification are from Sentinel-2, which is a publicly data from the European Space Agency. In total, 133,080 samples were analyzed using stratified random sampling, divided into training (70%) and test (30%) subsets. The accuracy is assessed by a variety of indicators. It is found that RF has the highest overall accuracy while the SVM has the lowest accuracy. The ANN has more advantages compared to others. Future agricultural land cover classifications can be based on the current research.","abstract_has_math":false,"creators":["Zhou, Xin"],"institution":"Faculty of Graduate Studies and Research, University of Regina","degree_name":"Master of Applied Science (MASc)","degree_level":"Master's","degree_discipline":"Engineering - Environmental Systems","degree_department":null,"school":null,"contributors":[],"advisors":["Wu, Peng"],"committee_chairs":[],"committee_members":["McCullum, Kevin","Veawab, Amornvadee","Han, Todd"],"year":2022,"date_issued":"2022-04-13","date_published":"2022-04-13","updated_at":"2026-07-24T04:03:45Z","subjects":[],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier.doi","label":"DOI","values":["https://doi.org/10.82465/4821"],"render_values":[{"text":"https://doi.org/10.82465/4821","href":"https://doi.org/10.82465/4821","code":true}]}]},"links":{"outbound_url":"https://hdl.handle.net/10294/15543","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Wu, Peng"]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["McCullum, Kevin","Veawab, Amornvadee","Han, Todd"]},{"key":"dc:creator","label":"Author","values":["Zhou, Xin"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2022-12-09T21:04:46Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2022-12-09T21:04:46Z"]},{"key":"dc:date.issued","label":"Date","values":["2022-04-13"]},{"key":"dc:publisher","label":"Institution","values":["Faculty of Graduate Studies and Research, University of Regina"]},{"key":"dc:type","label":"Dc Type","values":["master thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Engineering - Environmental Systems"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Master's"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Applied Science (MASc)"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Faculty of Graduate Studies and Research, University of Regina"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.doi","label":"DOI","values":["https://doi.org/10.82465/4821"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10294/15543"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["A Thesis Submitted to the Faculty of Graduate Studies and Research In Partial Fulfillment of the Requirements for the Degree of Master of Applied Science in Environmental Systems Engineering, University of Regina. 7, 109 p."]},{"key":"dc:description.abstract","label":"Abstract","values":["Land cover classification is critical in remote sensing. Reliable classification on land cover is required to address a wide range of environmental issues. Over recent years, the application of machine learning techniques in remote sensing has attracted wide attention. Machine learning has the capacity to classify land cover in remotely sensed photos effectively and efficiently. Machine learning techniques, such as Random Forests (RF), Support Vector Machines (SVM) and Artificial Neural Networks (ANN) can be applied in land cover classification. However, putting a machine learning categorization system in place is not easy, especially in the field of agricultural land classifications. Very limited research can be found using the machine learning techniques for agriculture land cover classifications. In the Canadian prairies, the cropping systems are based on simplified input-driven production of annual crops, which could affect land cover classification. To investigate the performance of machine learning techniques, the present research is conducted in the southern prairie region of Saskatchewan. The satellite images used for classification are from Sentinel-2, which is a publicly data from the European Space Agency. In total, 133,080 samples were analyzed using stratified random sampling, divided into training (70%) and test (30%) subsets. The accuracy is assessed by a variety of indicators. It is found that RF has the highest overall accuracy while the SVM has the lowest accuracy. The ANN has more advantages compared to others. Future agricultural land cover classifications can be based on the current research."]},{"key":"dc:title","label":"Title","values":["Comparison of Random Forests, Support Vector Machine and Artificial Neural Network Methods for Agriculture Land Cover Classification"]}]}],"canonical_facts":{"dc:contributor.advisor":["Wu, Peng"],"dc:contributor.committeemember":["McCullum, Kevin","Veawab, Amornvadee","Han, Todd"],"dc:creator":["Zhou, Xin"],"dc:date.accessioned":["2022-12-09T21:04:46Z"],"dc:date.available":["2022-12-09T21:04:46Z"],"dc:date.issued":["2022-04-13"],"dc:description":["A Thesis Submitted to the Faculty of Graduate Studies and Research In Partial Fulfillment of the Requirements for the Degree of Master of Applied Science in Environmental Systems Engineering, University of Regina. 7, 109 p."],"dc:description.abstract":["Land cover classification is critical in remote sensing. Reliable classification on land cover is required to address a wide range of environmental issues. Over recent years, the application of machine learning techniques in remote sensing has attracted wide attention. Machine learning has the capacity to classify land cover in remotely sensed photos effectively and efficiently. Machine learning techniques, such as Random Forests (RF), Support Vector Machines (SVM) and Artificial Neural Networks (ANN) can be applied in land cover classification. However, putting a machine learning categorization system in place is not easy, especially in the field of agricultural land classifications. Very limited research can be found using the machine learning techniques for agriculture land cover classifications. In the Canadian prairies, the cropping systems are based on simplified input-driven production of annual crops, which could affect land cover classification. To investigate the performance of machine learning techniques, the present research is conducted in the southern prairie region of Saskatchewan. The satellite images used for classification are from Sentinel-2, which is a publicly data from the European Space Agency. In total, 133,080 samples were analyzed using stratified random sampling, divided into training (70%) and test (30%) subsets. The accuracy is assessed by a variety of indicators. It is found that RF has the highest overall accuracy while the SVM has the lowest accuracy. The ANN has more advantages compared to others. 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