Skip to main content

Improved classification and segmentation of hyperspectral images using spectral warping

Buy Article:

$55.00 plus tax (Refund Policy)

This letter describes a method to increase hyperspectral image classification accuracy (CA) and segmentation accuracy (SA) using spectral warping, which is a nonlinear transformation that warps the frequency content of a signal. In the proposed approach, the frequency content corresponding to spectral data for the hyperspectral image was nonlinearly transformed along the spectral axis using warping. Classification and segmentation algorithms were estimated for the transformed spectral values to show the impact of warping. Experimental results are provided for different values of the warping parameter and it is shown that applying spectral warping increases CA and SA for appropriate warping parameters.
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), Electronics and Telecommunications Engineering Department, 41040 Kocaeli, Turkey

Publication date: 2008-06-01

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