We have modified the Fuzzy C-Means algorithm for an application related to segmentation of hyperspectral images. Classical fuzzy c-means algorithm uses Euclidean distance for computing sample membership to each cluster. We have introduced a different distance metric, Spectral Similarity Value (SSV), in order to have a more convenient similarity measure for reflectance information. SSV distance metric considers both magnitude difference (by the use of Euclidean distance) and spectral shape (by the use of Pearson correlation). Experiments confirmed that the introduction of this metric improves the quality of hyperspectral image segmentation, creating spectrally more dense clusters and increasing the number of correctly classified pixels.

Date Published: 2 February 2012
PDF: 10 pages
Proc. SPIE 8300, Image Processing: Machine Vision Applications V, 83000J (2 February 2012); doi: 10.1117/12.909081

Published in SPIE Proceedings Vol. 8300:
Image Processing: Machine Vision Applications V
Philip R. Bingham; Edmund Y. Lam, Editor(s)