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- W2072347595 abstract "In this paper, we propose an unsupervised segmentation method for hyperspectral images using mean shift filtering.One major problem of traditional mean shift algorithms is the difficulty of determining kernel bandwidths. We addressthis problem by using efficient clustering methods. First, PCA (Principal Component Analysis) was applied tohyperspectral images and the first three eigenimages were selected. Then, we applied mean shift filtering to the selectedimages using a kernel with a small bandwidth. This procedure produced a large number of clusters. In order to merge thehomogeneous clusters, we used the Bhattacharyya distance. Experiments showed promising segmentation resultswithout requiring user input." @default.
- W2072347595 created "2016-06-24" @default.
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- W2072347595 date "2010-08-19" @default.
- W2072347595 modified "2023-09-29" @default.
- W2072347595 title "Unsupervised segmentation for hyperspectral images using mean shift segmentation" @default.
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- W2072347595 doi "https://doi.org/10.1117/12.862176" @default.
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