Yousefi, Paria and Jalab, Hamid Abdullah and Ibrahim, Rabha Waell and Mohd Noor, Nurul Fazmidar and Ayub, Mohamad Nizam and Gani, Abdullah (2018) Water-body segmentation in satellite imagery applying modified Kernel K-means. Malaysian Journal of Computer Science, 31 (2). pp. 143-154. ISSN 0127-9084, DOI https://doi.org/10.22452/mjcs.vol31no2.4.
Full text not available from this repository.Abstract
The main purpose of k-Means clustering is partitioning patterns into various homogeneous clusters by minimizing cluster errors, but the modified solution of k-Means can be recovered with the guidance of Principal Component Analysis (PCA). In this paper, the linear Kernel PCA guides k-Means procedure using filter to modify images in situations where some parts are missing by k-Means classification. The proposed method consists of three steps: 1) transformation of the color space and using PCA to solve the eigenvalue problem pertaining to the covariance matrices of satellite image; 2) feature extraction from selected eigenvectors and are rearranged by applying the training map to extract the useful information as a set of new orthogonal variables called principal components; and 3) classification of the images based on the extracted features using k-Means clustering. The quantitative results obtained using the proposed method were compared with k-Means and k-Means PCA techniques in terms of accuracy in extraction. The contribution of this approach is the modification of PCA selection to achieve more accurate extraction of the water-body segmentation in satellite images.
| Item Type: | Article |
|---|---|
| Funders: | UNSPECIFIED |
| Uncontrolled Keywords: | Contextual filter; Image segmentation; K-Means clustering; PCA; Satellite images; Statistical pattern recognition; Water feature extraction |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | Faculty of Computer Science & Information Technology |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 12 Feb 2019 01:12 |
| Last Modified: | 12 Feb 2019 01:12 |
| URI: | http://eprints.um.edu.my/id/eprint/20257 |
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