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 t...Show MoreMetadata
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 G 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.
Date of Conference: 10-15 July 2016
Date Added to IEEE Xplore: 03 November 2016
ISBN Information:
Electronic ISSN: 2153-7003