Share Email Print

Journal of Applied Remote Sensing

Segmentation-based method for blind evaluation of noise variance in images
Author(s): Sergey Klavdievich Abramov; Vladimir Vasilyevich Lukin; Benoit Vozel; Kacem Chehdi; Jaakko T. Astola
Format Member Price Non-Member Price
PDF $20.00 $25.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

Noise is one of the basic factors that degrade remote sensing (RS) data and prevent accurate and reliable retrieval of useful information. Availability of a priori information on noise type and properties allows applying more effective methods for image processing, namely, filtering, edge detection, feature extraction, etc. However, noise statistics are often unknown and are to be estimated for an image at hand. Thus one needs blind methods for the evaluation of the noise variance, especially if the number of images or sub-band images of multichannel RS data is large enough. In this paper, we consider several approaches to blind evaluation of noise variance. An important item is that we consider both i.i.d. and spatially correlated noise. It is demonstrated that some techniques that perform well enough for i.i.d. noise fail if the image is corrupted by spatially correlated noise. We show how segmentation-based methods for blind evaluation of noise variance that operate in the spatial domain can be modified in order to provide better accuracy for wide ranges of noise variance and spatial correlation parameters. Numerical simulation results comparing the performance of several techniques are presented. Real RS data processing examples are also given.

Paper Details

Date Published: 1 August 2008
PDF: 16 pages
J. Appl. Rem. Sens. 2(1) 023533 doi: 10.1117/1.2977788
Published in: Journal of Applied Remote Sensing Volume 2, Issue 1
Show Author Affiliations
Sergey Klavdievich Abramov, National Aerospace Univ. (Ukraine)
Vladimir Vasilyevich Lukin, National Aerospace Univ. (Ukraine)
Benoit Vozel, Univ. de Rennes I (France)
Kacem Chehdi, Univ. de Rennes I (France)
Jaakko T. Astola, Tampere Univ. of Technology (Finland)

© SPIE. Terms of Use
Back to Top