Using vector agents to implement an unsupervised image classification algorithm
Borna, Kambiz; Moore, T.; Azadeh, N.H.; Pascal, S.
Date
2021-12-02Citation:
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/rs13234896Permanent link to Research Bank record:
https://hdl.handle.net/10652/5724Abstract
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.
Keywords:
image processing, image classification, unsupervised image classification, dynamic geometry, vector agents, modellingANZSRC Field of Research:
460306 Image processingCopyright Holder:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.Available Online at:
https://www.mdpi.com/2072-4292/13/23/4896Rights:
This digital work is protected by copyright. It may be consulted by you, provided you comply with the provisions of the Act and the following conditions of use. These documents or images may be used for research or private study purposes. Whether they can be used for any other purpose depends upon the Copyright Notice above. You will recognise the author's and publishers rights and give due acknowledgement where appropriate.Metadata
Show detailed recordThis item appears in
The following license files are associated with this item: