Signal processing with alpha-stable distributions and applications
Signal processing with alpha-stable distributions and applications
The GA0 Distribution as the True Model for SAR Images
SIBGRAPI '99 Proceedings of the XII Brazilian Symposium on Computer Graphics and Image Processing
Analysis of minute features in speckled imagery with maximum likelihood estimation
EURASIP Journal on Applied Signal Processing
Robust Principal Components for Hyperspectral Data Analysis
ICIAR '09 Proceedings of the 6th International Conference on Image Analysis and Recognition
Polarimetric SAR image segmentation with B-splines and a new statistical model
Multidimensional Systems and Signal Processing
Original Articles: Nonparametric edge detection in speckled imagery
Mathematics and Computers in Simulation
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The GA0 distribution is assumed as the universal model for multilook amplitude SAR imagery data under the multiplicative model. This distribution has two unknown parameters related to the roughness and the scale of the signal, that can be used in image analysis and processing. It can be seen that maximum likelihood and moment estimators for its parameters can be influenced by small percentages of "outliers"; hence, it is of outmost importance to find robust estimators for these parameters. One of the best-known classes of robust techniques is that of M-estimators, which are an extension of the maximum likelihood estimation method. In this work we derive the M-estimators for the parameters of the GA0 distribution, and compare them with maximum likelihood estimators with a Monte-Carlo experience. It is checked that this robust technique is superior to the classical approach under the presence of corner reflectors, a common source of contamination in SAR images. Numerical issues are addressed, and a practical example is provided.