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Proceedings Paper

Unsupervised segmentation for hyperspectral images using mean shift segmentation
Author(s): Sangwook Lee; Chulhee Lee
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Paper 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 address this problem by using efficient clustering methods. First, PCA (Principal Component Analysis) was applied to hyperspectral images and the first three eigenimages were selected. Then, we applied mean shift filtering to the selected images using a kernel with a small bandwidth. This procedure produced a large number of clusters. In order to merge the homogeneous clusters, we used the Bhattacharyya distance. Experiments showed promising segmentation results without requiring user input.

Paper Details

Date Published: 24 August 2010
PDF: 6 pages
Proc. SPIE 7810, Satellite Data Compression, Communications, and Processing VI, 781011 (24 August 2010); doi: 10.1117/12.862176
Show Author Affiliations
Sangwook Lee, Yonsei Univ. (Korea, Republic of)
Chulhee Lee, Yonsei Univ. (Korea, Republic of)


Published in SPIE Proceedings Vol. 7810:
Satellite Data Compression, Communications, and Processing VI
Bormin Huang; Antonio J. Plaza; Joan Serra-Sagristà; Chulhee Lee; Yunsong Li; Shen-En Qian, Editor(s)

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