Optimizing features based on object-oriented classification of a high-resolution urban image from the ZY-3 satellite
dc.contributor.advisor
Nichol, Carlline
en
dc.contributor.author
Yang, Tianyu
en
dc.date.accessioned
2021-05-13T15:29:45Z
dc.date.available
2021-05-13T15:29:45Z
dc.date.issued
2020-08-31
dc.description.abstract
There is a growing tendency for the utilization of high-resolution images due to the development of the sensors. However, the object-oriented classification cannot meet the accuracy requirement of the high-resolution images due to a large number of irrelevant and unnecessary features. In this dissertation, Yinchuan has been selected as the study area to explore the result of classification of optimized features (11 features) and initial features (41 features) based in object-oriented method on ZY-3 satellite. Correlated-based Feature Selection have been used to optimized features to reduce the abundant and irrelevant features. As a result, the classification of overall accuracy and kappa coefficient of optimized features have improved 6.44% and 8.45% respectively. In addition, the visual effect and processing time of optimized features have also been enhanced to a different extent. Therefore, the combination of feature selection and object-oriented method is better to enhance the classification accuracy of the high-resolution images.
en
dc.identifier.uri
https://hdl.handle.net/1842/37616
dc.identifier.uri
http://dx.doi.org/10.7488/era/897
dc.language.iso
en
dc.publisher
The University of Edinburgh
en
dc.subject
Feature selection
dc.subject
CFS
dc.subject
ZY-3
dc.subject
Object-oriented classification
dc.title
Optimizing features based on object-oriented classification of a high-resolution urban image from the ZY-3 satellite
en
dc.type
Thesis or Dissertation
en
dc.type.qualificationlevel
Masters
en
dc.type.qualificationname
MSc Master of Science
en
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