Journal
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
Volume 6, Issue 4, Pages 772-776Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LGRS.2009.2025059
Keywords
Change detection; k-means clustering; multitemporal satellite images; optical images; principal component analysis (PCA); remote sensing
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Funding
- Singapore Ministry of Education [R-143-000-358-112]
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In this letter, we propose a novel technique for unsupervised change detection in multitemporal satellite images using principal component analysis (PCA) and k-means clustering. The difference image is partitioned into h x h nonoverlapping blocks. S, S <= h(2), orthonormal eigenvectors are extracted through PCA of h x 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 x h difference image data onto the generated eigenvector space. The change detection is achieved by partitioning the feature vector space into two clusters using k-means clustering with k = 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.
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