出版社：University of Malaya * Faculty of Computer Science and Information Technology
摘要：The main purpose of kMeans clustering is partitioning patterns into various homogeneous clusters by minimizing cluster errors, but the modified solution of kMeans can be recovered with the guidance of Principal Component Analysis (PCA). In this paper, the linear Kernel PCA guides kMeans procedure using filter to modify images in situations where some parts are missing by kMeans 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 kMeans clustering. The quantitative results obtained using the proposed method were compared with kMeans and kMeans 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 waterbody segmentation in satellite images.
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