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Unsupervised segmentation of hyperspectral images using modified phase correlation
Date
2006-10-01
Author
Ertuerk, Alp
Ertuerk, Sarp
Metadata
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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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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.