Comparison of Random Forests, Support Vector Machine and Artificial Neural Network Methods for Agriculture Land Cover Classification

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.

Name thesis:degree_name

Master of Applied Science (MASc)

Level thesis:degree_level

Master's

Discipline thesis:degree_discipline

Engineering - Environmental Systems

Grantor dc:publisher

Faculty of Graduate Studies and Research, University of Regina

Year dc:date.issued

2022

Author dc:creator

• Zhou, Xin

Advisor dc:contributor.advisor

• Wu, Peng

Committee members dc:contributor.committeemember

• McCullum, Kevin
• Veawab, Amornvadee
• Han, Todd

Language dc:language.iso

en

DOI

https://doi.org/10.82465/4821

OAI identifier oai:identifier

oai:uregina.scholaris.ca:10294/15543