Please use this identifier to cite or link to this item:
https://doi.org/10.1109/LGRS.2009.2025059
DC Field | Value | |
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dc.title | Unsupervised change detection in satellite images using principal component analysis and κ-means clustering | |
dc.contributor.author | Celik, T. | |
dc.date.accessioned | 2014-10-16T08:47:08Z | |
dc.date.available | 2014-10-16T08:47:08Z | |
dc.date.issued | 2009-10 | |
dc.identifier.citation | Celik, T. (2009-10). Unsupervised change detection in satellite images using principal component analysis and κ-means clustering. IEEE Geoscience and Remote Sensing Letters 6 (4) : 772-776. ScholarBank@NUS Repository. https://doi.org/10.1109/LGRS.2009.2025059 | |
dc.identifier.issn | 1545598X | |
dc.identifier.uri | http://scholarbank.nus.edu.sg/handle/10635/95380 | |
dc.description.abstract | 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. | |
dc.description.uri | http://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1109/LGRS.2009.2025059 | |
dc.source | Scopus | |
dc.subject | κ-means clustering | |
dc.subject | Change detection | |
dc.subject | Multitemporal satellite images | |
dc.subject | Optical images | |
dc.subject | Principal component analysis (PCA) | |
dc.subject | Remote sensing | |
dc.type | Article | |
dc.contributor.department | CHEMISTRY | |
dc.description.doi | 10.1109/LGRS.2009.2025059 | |
dc.description.sourcetitle | IEEE Geoscience and Remote Sensing Letters | |
dc.description.volume | 6 | |
dc.description.issue | 4 | |
dc.description.page | 772-776 | |
dc.identifier.isiut | 000270761500033 | |
Appears in Collections: | Staff Publications |
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