Paper Info
Title
An unsupervised method for quality assessment of despeckling: an evaluation on COSMO-SkyMed data
Abstract
Goal of this paper is the development and evaluation of a fully automatic method for quality assessment of despeckled synthetic aperture radar (SAR) images. The rationale of the new approach is that any structural perturbation introduced by despeckling, e. g. a local bias of mean or the blur of a sharp edge or the suppression of a point target, may be regarded either as the introduction of a new structure or as the suppression of an existing one. Conversely, plain removal of random noise does not change structures in the image. Structures are identified as clusters in the scatterplot of original to filtered image. Ideal filtering should produce clusters all aligned along the main diagonal. In practice clusters are moved far from the diagonal. Clusters' centers are detected through the mean shift algorithm. A structural change feature is defined at each pixel from the position and population of off-diagonal cluster, according to Shannon's information theoretic concepts. Results on true SAR images (COSMO-SkyMed) will be presented. Bayesian estimators (LMMSE: liner minimum mean squared error: MAP: maximum alpha-posteriori probability) operating in the undecimated wavelet domain have been coupled with segment-based processing. Quality measurements of despeckled SAR images carried out by means of the proposed method highlight the benefits of segmented MAP filtering.
Year
DOI
Venue
2011
10.1117/12.898534
Proceedings of SPIE
Keywords
Field
DocType
Clustering,despeckling,mean shift algorithm,multivariate analysis,quality measurements,SAR imagery
Population,Pattern recognition,Synthetic aperture radar,Filter (signal processing),Minimum mean square error,Artificial intelligence,Pixel,Mean-shift,Geography,Estimator,Wavelet
Conference
Volume
ISSN
Citations 
8179
0277-786X
2
PageRank 
References 
Authors
0.43
0
6
Name
Order
Citations
PageRank
Bruno Aiazzi127527.84
Luciano Alparone290180.27
Fabrizio Argenti317426.24
Stefano Baronti455950.87
Tiziano Bianchi5100362.55
alberto lapini620.77