V. Krylov, G. Moser, S. B. Serpico, J. Zerubia.
"Dictionary-based probability density function estimation for high-resolution SAR data".
IS&T/SPIE Electronic Imaging 2009,
Proc. of SPIE, volume 7246, 72460S, San Jose (USA), January 18-22, 2009.
[link] [pdf]
Abstract
In the context of remotely sensed data analysis, a crucial problem is represented by the need to develop accurate models for the statistics of pixel intensities. In this work, we develop a parametric finite mixture model for the statistics of pixel intensities in high resolution synthetic aperture radar (SAR) images. This method is an extension of previously existing method for lower resolution images. The method integrates the stochastic expectation maximization (SEM) scheme and the method of log-cumulants (MoLC) with an automatic technique to select, for each mixture component, an optimal parametric model taken from a predefined dictionary of parametric probability density functions (pdf). The proposed dictionary consists of eight state-of-the-art SAR- specific pdfs: Nakagami, log-normal, generalized Gaussian Rayleigh, Heavy-tailed Rayleigh, Weibull, K-root, Fisher and generalized Gamma. The designed scheme is endowed with the novel initialization procedure and the algorithm to automatically estimate the optimal number of mixture components. The experimental results with a set of several high resolution COSMO-SkyMed images demonstrate the high accuracy of the designed algorithm, both from the viewpoint of a visual comparison of the histograms, and from the viewpoint of quantitive accuracy measures such as correlation coefficient. The method proves to be effective on all the considered images, remaining accurate for multimodal and highly heterogeneous scenes.
Bibtex
@INPROCEEDINGS{KrylovSPIE09,
author = {Krylov, V. and Moser, G. and Serpico, S. B. and Zerubia, J.},
title = {Dictionary-based probability density function estimation for high-resolution {SAR} data},
year = {2009},
booktitle = {Proceedings of SPIE},
volume = {7246},
address = {San Jose, USA},
pages = {72460S}
}