Skip to main content
padlock icon - secure page this page is secure

Phase-correlation-based hyperspectral image classification using multiple class representatives obtained with k-means clustering

Buy Article:

$60.00 + tax (Refund Policy)

In this letter, a modification to a phase-correlation-(PC-)based supervised classification method for hyperspectral data is proposed. An adaptive approach using different numbers of multiple class representatives (CRs) extracted using PC-based k-means clustering for each class is compared with the use of selecting a small, pre-determined number of dissimilar CRs. PC is used as a distance measure in k-means clustering to determine the spectral similarity between each pixel and cluster centre. The number of representatives for each class is chosen adaptively, depending on the number of training samples in each class. Classification is performed for each pixel according to the maximum value of PCs obtained between test samples and the CRs. Experimental results show that the adaptive method gave the highest classification accuracy (CA). Experiments on the effect of reducing the size of the feature vectors found that CA increased as the feature vector decreased.
No Reference information available - sign in for access.
No Citation information available - sign in for access.
No Supplementary Data.
No Article Media
No Metrics

Document Type: Research Article

Affiliations: Kocaeli University Laboratory of Image and Signal processing (KULIS), Department of Electronic and Telecommunication Engineering, University of Kocaeli, 41040 Kocaeli, Turkey

Publication date: January 1, 2009

More about this publication?
  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
X
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more