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Publication Details
AFRICAN RESEARCH NEXUS
SHINING A SPOTLIGHT ON AFRICAN RESEARCH
earth and planetary sciences
Unsupervised change detection in satellite images using principal component analysis and κ-means clustering
IEEE Geoscience and Remote Sensing Letters, Volume 6, No. 4, Article 5196726, Year 2009
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Description
In this letter, we propose a novel technique for unsupervised change detection in multitemporal satellite images using principal component analysis (PCA) and κ-means clustering. The difference image is partitioned into h ×h nonoverlapping blocks. S,S≤h2, orthonormal eigenvectors are extracted through PCA of h×h nonoverlapping block set to create an eigenvector space. Each pixel in the difference image is represented with an S-dimensional feature vector which is the projection of h×h difference image data onto the generated eigenvector space. The change detection is achieved by partitioning the feature vector space into two clusters using κ-means clustering with κ = 2 and then assigning each pixel to the one of the two clusters by using the minimum Euclidean distance between the pixel's feature vector and mean feature vector of clusters. Experimental results confirm the effectiveness of the proposed approach. © 2009 IEEE.
Authors & Co-Authors
Çelik, Turgay
Singapore, Singapore City
National University of Singapore
Statistics
Citations: 820
Authors: 1
Affiliations: 1
Identifiers
Doi:
10.1109/LGRS.2009.2025059
ISSN:
1545598X