By Topic

A low computational-cost method to fuse IKONOS images using the spectral response function of its sensors

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Gonzalez-Audicana, M. ; Centre de Visio per Computador, Univ. Autonoma de Barcelona, Spain ; Otazu, X. ; Fors, O. ; Alvarez-Mozos, J.

Probably the most popular image fusion method is that based on the intensity-hue-saturation (IHS) transform. Although the spatial enhancement of the IHS-merged images is high, the distortion of its spectral information may also be important. In recent years, several methods have been developed to minimize this problem, being those based on wavelets widely used. However, the high computational cost of these approaches makes them unattractive to applications that involve fast merging of very large volumes of data. In this paper, we present a low computational-cost image fusion method based on the fast IHS transform, which uses the information of the spectral response functions of the low-resolution multispectral (LRM) and high-resolution panchromatic (HRP) sensors to minimize the spectral distortion problem. Using this information, we directly obtain from the HRP image the intensity image that the LRM sensor would observe if it worked at a spatial resolution similar to that of the HRP image. The experimental results carried out on IKONOS images demonstrate that the proposed approach can perform as well as wavelet-based approaches with a lower computational cost.

Published in:

Geoscience and Remote Sensing, IEEE Transactions on  (Volume:44 ,  Issue: 6 )