Paper Info
Title
Unsupervised Change Detection On Synthetic Aperture Radar Images With Generalized Gamma Distribution
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(G Gamma D). G Gamma D 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 G Gamma D-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.
Year
DOI
Venue
2016
10.1109/IGARSS.2016.7729866
2016 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS)
Keywords
Field
DocType
Unsupervised change detection, method of log-cumulants, consistent estimator, SAR
Computer vision,Histogram,Change detection,Parametric model,Computer science,Synthetic aperture radar,Remote sensing,Inverse synthetic aperture radar,Artificial intelligence,Gamma distribution,Image resolution,Generalized gamma distribution
Conference
ISSN
Citations 
PageRank 
2153-6996
0
0.34
References 
Authors
5
4
Name
Order
Citations
PageRank
Fabrizio Crismer100.34
Gabriele Moser291976.92
Vladimir A. Krylov313314.81
Sebastiano B. Serpico474964.86