WATER-BODY SEGMENTATION IN SATELLITE IMAGERY APPLYING MODIFIED KERNEL KMEANS
| Autor: | Abdullah Gani, Mohamad Nizam Ayub, Rabha W. Ibrahim, Nurul Fazmidar Mohd Noor, Paria Yousefi, Hamid A. Jalab |
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| Rok vydání: | 2018 |
| Předmět: |
010504 meteorology & atmospheric sciences
General Computer Science business.industry Computer science Feature extraction 0211 other engineering and technologies k-means clustering Pattern recognition 02 engineering and technology Image segmentation 01 natural sciences Kernel principal component analysis ComputingMethodologies_PATTERNRECOGNITION Kernel (image processing) Principal component analysis Segmentation Artificial intelligence Cluster analysis business 021101 geological & geomatics engineering 0105 earth and related environmental sciences |
| Zdroj: | Malaysian Journal of Computer Science. 31:143-154 |
| ISSN: | 0127-9084 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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