Unsupervised segmentation of hyperspectral images using modified phase correlation

Date

2006-10-01

Author

Ertuerk, Alp
Ertuerk, Sarp

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This letter presents hyperspectral image segmentation based on the phase-correlation measure of subsampled hyperspectral data, which is referred to as modified phase correlation. The hyperspectral spectrum of each pixel is initially subsampled to gain, robustness against noise and spatial variability, and phase correlation is applied to determine spectral similarity. Similar and dissimilar pixels are decided according to the peak value of the phase correlation result to determine pixels that fall into the same segments. The approach can be regarded as a region-growing technique. The total number of segments is determined automatically according to the similarity threshold.

Subject Keywords

Hyperspectral image segmentation, Phase correlation

URI

https://hdl.handle.net/11511/65150

Journal

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS

DOI

https://doi.org/10.1109/lgrs.2006.880535

Collections

Department of Electrical and Electronics Engineering, Article

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Citation Formats

A. Ertuerk and S. Ertuerk, “Unsupervised segmentation of hyperspectral images using modified phase correlation,” IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, pp. 527–531, 2006, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/65150.