F. Crismer, G. Moser, V. A. Krylov, S. B. Serpico. "Unsupervised Change Detection on Synthetic Aperture Radar Images with Generalized Gamma Distribution",
IEEE Geoscience and Remote Sensing Symposium IGARSS 2016,
Proc. of IEEE IGARSS 2016, Beijing (China), July 10-16, 2016.
[link] [pdf] [presentation]
Abstract
The availability of synthetic aperture radar (SAR) data with high spatial resolution offers great potential for environmental monitoring due to the insensitivity of SAR to atmospheric and sunlight-illumination conditions. In this paper, an unsupervised change detection method for SAR images at medium to high resolution is proposed. The image ratioing approach is adopted, and a Bayesian unsupervised minimum-error thresholding algorithm is extended by proposing a technique based on Generalized Gamma distributions (GΓD). GΓD was recently found to be an accurate model for the statistics of SAR amplitudes at moderate to high resolution. Here, a specific parametric modeling approach for the ratio of GΓD-distributed SAR images is proposed and endowed with a probability density function estimation algorithm based on the method of log-cumulants. Consistency of this estimator is proven. Experimental results confirm the accuracy of the method for medium and high resolutions X-band SAR images.
Bibtex
@INPROCEEDINGS{SerpicoIGARSS12,
author = {F. Crismer and G. Moser and V. A. Krylov and S. B. Serpico},
title = {Unsupervised Change Detection on Synthetic Aperture Radar Images with Generalized Gamma Distribution},
year = {2016},
booktitle = {Proc. of IEEE IGARSS},
address = {Beijing, China},
}