By Topic

Nonwhite Noise Reduction in Hyperspectral Images

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

3 Author(s)
Xuefeng Liu ; Ecole Centrale Marseille & Fresnel Inst., Marseille, France ; Bourennane, S. ; Fossati, C.

Noise reduction is an important preprocessing step to analyze the information in the hyperspectral image (HSI). Because the common filtering methods for HSIs are based on the data vectorization or matricization while ignoring the related information between image planes, there are new approaches considering multidimensional data as whole entities, for example, multidimensional Wiener filtering (MWF) based on Tucker3 tensor decomposition. However, if HSIs are not disturbed by white noise, MWF cannot effectively remove the nonwhite noise and obtain the expected signal. To reduce nonwhite noise from HSIs, a new method is proposed in this letter. The first step of this method is to whiten the noise in HSIs through a prewhitening procedure. Then, MWF can help to denoise the prewhitened data. At last, an inverse prewhitening process can rebuild the estimated signal. Comparative studies with existing denoising methods show that the proposed approach has promising prospects in this field.

Published in:

Geoscience and Remote Sensing Letters, IEEE  (Volume:9 ,  Issue: 3 )