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Paria Yousefi Hamid A. Jalab Rabha W. Ibrahim Nurul F. Mohd Noor Mohamad N. Ayub Abdullah Gani


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.

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YOUSEFI, Paria et al. WATER-BODY SEGMENTATION IN SATELLITE IMAGERY APPLYING MODIFIED KERNEL KMEANS. Malaysian Journal of Computer Science, [S.l.], v. 31, n. 2, p. 143-154, apr. 2018. ISSN 0127-9084. Available at: <https://ejournal.um.edu.my/index.php/MJCS/article/view/11640>. Date accessed: 21 may 2018. doi: https://doi.org/10.22452/mjcs.vol31no2.4.