Unsupervised segmentation for hyperspectral images using mean shift segmentation

Sangwook Lee; Chulhee Lee

doi:10.1117/12.862176

24 August 2010 Unsupervised segmentation for hyperspectral images using mean shift segmentation

Sangwook Lee, Chulhee Lee

Proceedings Volume 7810, Satellite Data Compression, Communications, and Processing VI; 781011 (2010) https://doi.org/10.1117/12.862176
Event: SPIE Optical Engineering + Applications, 2010, San Diego, California, United States

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.

Citation Download Citation

Sangwook Lee and Chulhee Lee "Unsupervised segmentation for hyperspectral images using mean shift segmentation", Proc. SPIE 7810, Satellite Data Compression, Communications, and Processing VI, 781011 (24 August 2010); https://doi.org/10.1117/12.862176

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