Using vector agents to implement an unsupervised image classification algorithm
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Authors
Borna, Kambiz
Moore, T.
Azadeh, N.H.
Pascal, S.
Moore, T.
Azadeh, N.H.
Pascal, S.
Author ORCID Profiles (clickable)
Degree
Grantor
Date
2021-12-02
Supervisors
Type
Journal Article
Ngā Upoko Tukutuku (Māori subject headings)
Keyword
image processing
image classification
unsupervised image classification
dynamic geometry
vector agents
modelling
image classification
unsupervised image classification
dynamic geometry
vector agents
modelling
ANZSRC Field of Research Code (2020)
Citation
Borna, K., Moore, T.,Azadeh, N.H., & Pascal, S. (2021). Using vector agents to implement an unsupervised image classification algorithm. Remote Sensing, 13, 4896. doi:https://doi.org/10.3390/rs13234896
Abstract
Unsupervised image classification methods conventionally use the spatial information of pixels to reduce the effect of speckled noise in the classified map. To extract this spatial information, they employ a predefined geometry, i.e., a fixed-size window or segmentation map. However, this coding of geometry lacks the necessary complexity to accurately reflect the spatial connectivity within objects in a scene. Additionally, there is no unique mathematical formula to determine the shape and scale applied to the geometry, being parameters that are usually estimated by expert users. In this paper, a novel geometry-led approach using Vector Agents (VAs) is proposed to address the above drawbacks in unsupervised classification algorithms. Our proposed method has two primary steps: (1) creating reliable training samples and (2) constructing the VA model. In the first step, the method applies the statistical information of a classified image by k-means to select a set of reliable training samples. Then, in the second step, the VAs are trained and constructed to classify the image. The model is tested for classification on three high spatial resolution images. The results show the enhanced capability of the VA model to reduce noise in images that have complex features, e.g., streets, buildings.
Publisher
MDPI (Multidisciplinary Digital Publishing Institute)
Permanent link
Link to ePress publication
DOI
doi:https://doi.org/10.3390/rs13234896
Copyright holder
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.
Copyright notice
Attribution 4.0 International