DocumentCode :
969112
Title :
Spatially Nonstationary Anisotropic Texture Analysis in SAR Images
Author :
D´Hondt, Olivier ; López-Martínez, Carlos ; Ferro-Famil, Laurent ; Pottier, Eric
Author_Institution :
Univ. Politecnica de Catalunya, Barcelona
Volume :
45
Issue :
12
fYear :
2007
Firstpage :
3905
Lastpage :
3918
Abstract :
This paper deals with spatial analysis of texture in synthetic aperture radar (SAR) images. A new parametric model for local two-point statistics of the image is introduced, in order to characterize the spatially nonstationary and anisotropic behavior of the image. The texture is first modeled by a nonstationary Gaussian process resulting from the convolution of a Gaussian white noise with a field of anisotropic Gaussian kernel with spatially varying parameters. Hence, under the hypothesis of locally stationary signal, the analytic expression of the local autocovariance is derived. It is then explained how to simulate nonstationary K-distributed random fields by combining the new model with an already existing simulation method. A method for parameter estimation is then introduced. This method, based on the statistical product model, first corrects the speckle contribution to the local autocovariance and estimates the parameters of the model by analyzing the shape of the autocovariance. The algorithm is then evaluated over simulated and experimental data. Stationary simulations permit to show that, for a sufficient sample size, the estimator is unbiased. A test over a nonstationary simulation proves the ability of the algorithm to capture the spatial fluctuations of the texture. Finally, the method is applied to the experimental SAR data, and it is shown that a large amount of spatial information may be retrieved from the data.
Keywords :
Gaussian processes; image texture; parameter estimation; remote sensing by laser beam; synthetic aperture radar; white noise; Gaussian white noise; anisotropic Gaussian kernel; local autocovariance; local two-point statistics; locally stationary signal; nonstationary Gaussian process; nonstationary K-distributed random fields; parameter estimation; parametric model; spatial fluctuations; spatial information; spatially nonstationary anisotropic texture analysis; spatially varying parameters; statistical product model; synthetic aperture radar images; Anisotropic magnetoresistance; Convolution; Gaussian processes; Image analysis; Image texture analysis; Kernel; Parameter estimation; Parametric statistics; Synthetic aperture radar; White noise; Autocovariance modeling; feature extraction; image texture analysis; nonstationary random process; synthetic aperture radar (SAR);
fLanguage :
English
Journal_Title :
Geoscience and Remote Sensing, IEEE Transactions on
Publisher :
ieee
ISSN :
0196-2892
Type :
jour
DOI :
10.1109/TGRS.2007.908877
Filename :
4378535
Link To Document :
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