Cart (Loading....) | Create Account
Close category search window
 

Superresolution Construction of Multispectral Imagery Based on Local Enhancement

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

2 Author(s)
Elbakary, M. ; Dept. of Electr. & Comput. Eng., South Alabama Univ., Mobile, AL ; Alam, M.

Hyperspectral imagery is used for a wide variety of applications, including target detection, tracking, agricultural monitoring, and natural resources exploration. The main reason for using hyperspectral imagery is that images reveal spectral information about the scene that is not available in a single band. Unfortunately, many factors, such as the limitations of focal plane array technology, the inherent tradeoff in spatial versus spectral resolution, and the desire to achieve area coverage, degrade the spatial quality of these images. Recently, many algorithms are introduced in the literature to improve the resolution of hyperspectral images using coregistered high spatial resolution imagery such as panchromatic imagery. In this letter, we propose a new algorithm to enhance the spatial resolution of low-resolution hyperspectral bands using strongly correlated and coregistered high spatial resolution panchromatic imagery. The proposed algorithm constructs the superresolution bands corresponding to the low-resolution bands to enhance the resolution using a proposed local enhancement technique. The local enhancement is based on the least squares regression and the local correlation to improve the estimated interpolation of the spatial resolution. The introduced algorithm is considered as an improvement for Price's algorithm which uses the global correlation for the spatial resolution enhancement. In addition, numerous studies are conducted to investigate the effect of spatial window size for achieving the local enhancement in the estimation process. The experimental results, which are obtained using hyperspectral data derived from an airborne imaging sensor, are presented to verify the improvement by the proposed algorithm.

Published in:

Geoscience and Remote Sensing Letters, IEEE  (Volume:5 ,  Issue: 2 )

Date of Publication:

April 2008

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.