Unassisted Quantitative Evaluation of Despeckling Filters
Department of Electronic Engineering and Automation, University of Las Palmas de G.C., Las Palmas 35001, Spain
Departamento de Estatística. Universidade Federal de Pernambuco, Recife, PE 50670-901, Brazil
LaCCAN—Laboratório de Computação Científica e Análise Numérica, Universidade Federal de Alagoas, Maceió, AL 57072-900, Brazil
These authors contributed equally to this work.
Author to whom correspondence should be addressed.
Academic Editors: Qi Wang, Nicolas H. Younan, Carlos López-Martínez, Xiaofeng Li and Prasad S. Thenkabail
Received: 27 February 2017 / Revised: 30 March 2017 / Accepted: 13 April 2017 / Published: 20 April 2017
SAR (Synthetic Aperture Radar) imaging plays a central role in Remote Sensing due to, among other important features, its ability to provide high-resolution, day-and-night and almost weather-independent images. SAR images are affected from a granular contamination, speckle, that can be described by a multiplicative model. Many despeckling techniques have been proposed in the literature, as well as measures of the quality of the results they provide. Assuming the multiplicative model, the observed image Z
is the product of two independent fields: the backscatter X
and the speckle Y
. The result of any speckle filter is
, an estimator of the backscatter X
, based solely on the observed data Z
. An ideal estimator would be the one for which the ratio of the observed image to the filtered one
is only speckle: a collection of independent identically distributed samples from Gamma variates. We, then, assess the quality of a filter by the closeness of I
to the hypothesis that it is adherent to the statistical properties of pure speckle. We analyze filters through the ratio image they produce with regards to first- and second-order statistics: the former check marginal properties, while the latter verifies lack of structure. A new quantitative image-quality index is then defined, and applied to state-of-the-art despeckling filters. This new measure provides consistent results with commonly used quality measures (equivalent number of looks, PSNR, MSSIM,
edge correlation, and preservation of the mean), and ranks the filters results also in agreement with their visual analysis. We conclude our study showing that the proposed measure can be successfully used to optimize the (often many) parameters that define a speckle filter.
This is an open access article distributed under the Creative Commons Attribution License
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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MDPI and ACS Style
Gomez, L.; Ospina, R.; Frery, A.C. Unassisted Quantitative Evaluation of Despeckling Filters. Remote Sens. 2017, 9, 389.
Gomez L, Ospina R, Frery AC. Unassisted Quantitative Evaluation of Despeckling Filters. Remote Sensing. 2017; 9(4):389.
Gomez, Luis; Ospina, Raydonal; Frery, Alejandro C. 2017. "Unassisted Quantitative Evaluation of Despeckling Filters." Remote Sens. 9, no. 4: 389.
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