Mai, D.S. and Ngo, L.T. (2015) Semi-supervised fuzzy C-means clustering for change detection from multispectral satellite image. In: IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2015, 2 August 2015 through 5 August 2015.
36.Semi-Supervised Fuzzy C-Means Clustering for Change Detection from Multispectral Satellite Image.pdf
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Abstract
Data clustering has been applied in almost areas such as health, natural resource management, urban planning: especially, fuzzy clustering which the advantage with handling better for ambiguous data. This paper proposes a method of improving fuzzy c-means clustering algorithm by using the criteria to move the prototype of clusters to the expected centroids which are pre-determined on the basis of samples. The proposed algorithm is used for a model of change detection on multispectral satellite imagery at multiple temporals. The experiments are implemented on various data sets in comparison with other approaches. © 2015 IEEE.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Divisions: | Institutes > Institute of Techniques for Special Engineering Faculties > Faculty of Information Technology |
| Identification Number: | 10.1109/FUZZ-IEEE.2015.7337978 |
| Uncontrolled Keywords: | Fuzzy clustering; Fuzzy systems; Image segmentation; Information management; Natural resources management; Satellite imagery; Satellites; Signal detection; Change detection; Data clustering; Fuzzy C mean clustering; Fuzzy C means clustering; Fuzzy c-means clustering algorithms; Multispectral satellite image; Multispectral satellite imagery; Natural resource management; Clustering algorithms |
| Additional Information: | Conference code: 118450. Language of original document: English. |
| URI: | http://eprints.lqdtu.edu.vn/id/eprint/9896 |
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