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Comparison of Supervised Image Classification Algorithms: Classifying Diverse Land Cover in California
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| 2.67 MB | Adobe PDF |
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Orientador(es)
Resumo(s)
The research field of machine learning and supervised image classification is quickly developing. There
are many studies regarding the different use cases of image classification. However, a comprehensive
study on the primary algorithms in ArcGIS Pro has not been assessed for numerous classes. This study
attempts to bridge that gap by evaluating the effectiveness of the three primary classification
algorithms available in ArcGIS Pro, and to determine an optimal algorithm for the given study area.
This scope covers 12 classes of land cover in San Joaquin County, California. Maximum Likelihood,
Random Forest, and Support Vector Machine were tested based on their general usability in image
classification as well as their proven characteristics through research. The training and ground truth
validation data were provided by USGS, in the form of a Landsat 8 image, and crop planning map. The
accuracy assessment was performed with a stratified random sampling strategy. Based on the Kappa
statistic, this study determines Random Forest (Kappa = 0.68, Accuracy = 0.76) to be the most suitable
algorithm for detecting a series of crop types, bodies of water, and urban spaces apart from the rest
of the land cover in San Joaquin County, California, USA. In addition to determining a preferred
algorithm, it is also apparent that certain parameters when tweaked, produce the optimal classifier for
this dataset. In this case, this means most parameters set to default, with an increased spectral detail
and a decreased spatial detail. What this indicates for crop planning is that the current algorithms used
in California are already quite effective at accurately identifying unique types of land cover. This builds
confidence in the field, however parameters could be similarly tweaked to produce an even better
classification. This study can be useful for improving crop and water planning.
Descrição
Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business Analytics
This research was part of the EXPL/CTA-AMB/0165/2021 project, supported by a grant of the Portuguese Foundation for Science and Technology ("Fundação para a Ciência e a Tecnologia"): https://doi.org/10.54499/EXPL/CTA-AMB/0165/2021
This research was part of the EXPL/CTA-AMB/0165/2021 project, supported by a grant of the Portuguese Foundation for Science and Technology ("Fundação para a Ciência e a Tecnologia"): https://doi.org/10.54499/EXPL/CTA-AMB/0165/2021
Palavras-chave
Machine Learning Remote Image Sensing Geographic Information Systems Supervised Image Classification Algorithms SDG 8 - Decent work and economic growth SDG 12 - Responsible production and consumption SDG 13 - Climate action SDG 15 - Life on land