Name
Abstract | ||
|---|---|---|
In this work, segmentation of hyperspectral images by local covariance matrices in eigenspace has been proposed for getting high accuracy rates using unsupervised methods. Combination of both spectral and spatial features can increase the segmentation accuracy for hyperspectral images without groundtruth. Furthermore, changing from original data space to eigenspace via principal component analysis and its kernelized version and the calculation of covariance matrices in this new space can produce better results for different clustering methods. In the simulations, effects of local neighbors in the computation of covariance matrices in eigenspace were represented using four different clustering algorithms comparatively. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/SIU.2013.6531529 | Signal Processing and Communications Applications Conference |
Keywords | Field | DocType |
covariance matrices,feature extraction,image segmentation,pattern clustering,principal component analysis,clustering algorithms,clustering methods,eigenspace,high accuracy rates,hyperspectral image segmentation,hyperspectral images,kernelized version,local covariance matrices,principal component analysis,spatial features,spectral features,unsupervised methods,Hyperspectral images,local covariance matrices,segmentation,spectro-spatial features | Computer vision,Pattern recognition,Computer science,Segmentation,Matrix (mathematics),Image segmentation,Feature extraction,Hyperspectral imaging,Artificial intelligence,Cluster analysis,Principal component analysis,Covariance | Conference |
ISSN | ISBN | Citations |
2165-0608 | 978-1-4673-5561-2 | 0 |
PageRank | References | Authors |
0.34 | 11 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ugur Ergul | 1 | 3 | 2.74 |
| Gökhan Bilgin | 2 | 62 | 13.18 |