Anomaly detection in polarimetric radar images

Georgy Melamed*, Stanley R. Rotman, Dan G. Blumberg, Anthony J. Weiss

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


An unsupervised anomaly detection algorithm for synthetic aperture radar (SAR) images, making use of polarized data, is developed. The processing contains several stages, including calibration of the images, extraction of information parameters and speckle filtering, detection of candidate pixels and application of a constant false alarm rate (CFAR) morphology operator. The developed algorithm is independent of the anomaly's radar cross section (RCS); it depends only on the physical structure of the observed objects. The proposed processing is non-iterative, adaptive and semi-automatic. Performance evaluation shows improved performance of the algorithm over the common alternatives.

Original languageEnglish
Pages (from-to)1164-1189
Number of pages26
JournalInternational Journal of Remote Sensing
Issue number4
StatePublished - Feb 2012


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